Abstract
In the face of protracted crises like climate change or pandemics, the influence of expert scientific projections on public policy is crucial yet evolves over time. This study offers an empirical demonstration of a previously fragmented theory: the diminishing influence of scientific projections on policy over time. Using a comprehensive mixed-method analysis, the article studies the relationship between expert projections, policy stringency and public support in Quebec during the COVID-19 pandemic. Scientific projections that put forward worst-case scenarios have a considerable impact on policies made in the early stages of a crisis. However, as these catastrophic projections instil a sense of fatalism as the crisis lasts, they inadvertently lead to diminished public support for both the policies and the scientific projections themselves. The implications of these findings for scientists and experts are discussed, highlighting the importance of adapting projections and knowledge communication strategies as the crisis unfolds.
Introduction
The power of scientific experts in shaping public policy is well documented. Experts influence the formation of governmental agendas, problem definition, policy design, instrument choice and regulatory decisions (Jasanoff, 1990; Baumgartner and Jones, 1993; Rochefort and Cobb, 1993; Schneider and Ingram, 1997). What is less well known, however, is the duration of this influence and its relationship to other policy inputs, notably public opinion. Evidence suggests that the impact of scientific expertise on governmental decision-making is not consistent over time. For instance, in the context of the climate crisis, studies have highlighted the growing resistance to scientific advice from influential interest groups (Oreskes and Conway, 2010; Dryzek et al, 2011; Mildenberger and Leiserowitz, 2017). During the COVID-19 crisis, some research showed that the growth of scientific controversies eroded the influence of experts (Cairney, 2021; Armingeon and Sager, 2022; Easton et al, 2022; Eichenberger et al, 2023). Nevertheless, there is still a great deal of work to be done to theorise fluctuations in expert influence over time, a central aim of this article.
The COVID-19 crisis provides a valuable opportunity to build on this research. Not only did experts play a central role during the pandemic, but the decisions made were also grounded in a form of scientific knowledge particularly suited to support the policies implemented. However, integrating scientific information into public policy can be a challenge (Sarkki et al, 2014). The COVID-19 crisis allows us to focus on scientific projections, a type of scientific information that lends itself to immediate use by policy makers in the context of an emergency. Despite their utility, scientific projections may not maintain their influence throughout the entire duration of a crisis, partly due to their relationship with public opinion (Norgaard, 2011).To what extent can experts’ scientific projections, even when catastrophic, sustain their influence over time? And what mechanisms underpin this influence? These are the questions this article aims to address.
Using a unique and diverse data set, we empirically show that catastrophic expert projections do influence public policies, but their impact diminishes over time as public and interest-group support for government policies wanes. Our study focuses on Quebec’s COVID-19 response and utilises original data on expert projections of hospitalisations, public opinion, interest-group activity and public adherence to health measures (Rocheleau, 2017; Lemor et al, 2023; Lemor, 2024). We additionally analyse the full transcripts of all press conferences given by Quebec policy makers during the COVID-19 crisis.
Theory
Scientific modelling and projections occupy a central position in disciplines like climatology, biology and public health. Providing future estimations based on current conditions, they can also inform policy decisions. Projections and models, in climate change, for example, alert policy makers to potential consequences of inaction, such as failing to reduce CO2 emissions or prepare for extreme weather events (Pachauri et al, 2015; Martel-Morin and Lachapelle, 2022). Projections offer experts a platform to influence public policy through clear communication of complex actionable knowledge. In the realm of public health, epidemiological projections are instrumental during epidemics. Using mathematical models, they predict future health outcomes based on alternative health interventions (Djidjou-Demasse et al, 2020). Epidemiological projections provide policy makers with information on the impact of the various options available to them before they make a decision.
In the context of global warming, expert projections often depict catastrophic scenarios, such as the occurrence of extreme events (Koubi, 2019). Likewise, during the COVID-19 pandemic, experts provided a range of projections, with early scenarios suggesting catastrophic outcomes like hundreds of thousands of deaths and overwhelmed healthcare systems (Pueyo, 2020; James et al, 2021; Ioannidis et al, 2022). These catastrophic projections called for policy responses, which they successfully triggered. While it seems obvious that the catastrophic nature of scientific projections often induces an immediate response from decision makers, we might ask whether it encourages sustained political commitment over time.
Temporal dynamics of experts’ influence
At a crisis’s onset, the urgency to act against the problem can endow experts’ scientific projections with considerable influence (Auld et al, 2021: 710). The uncertainty inherent in the early stages of a crisis induces policy makers to welcome the advice of individual experts or epistemic communities (Haas, 1992; van Asselt and Vos, 2006; Lemor and Montpetit, 2024). During the COVID-19 pandemic, it was at the beginning of the crisis that expert recommendations indeed appeared to have been followed most by policy makers (Eichenberger et al, 2023), an observation also made in other health crises such as H1N1 (Salajan et al, 2020).
However, once the initial stages stage of the crisis has passed, once expert advice has successfully reduced uncertainty, the demand for expert information diminishes. The attention of policy makers then shifts towards public opinion, the competition of interests and other such consideration (Löblová, 2018: 181). Policy learning may be at the origin of the shift (Dunlop, 2014). Research has shown that in the early stages of problem solving, instrumental learning gives significant influence to experts. However, in subsequent stages, policy makers learn that solving problems is sometimes insufficient and need to give heed to psychosocial factors, including public discontent (Zaki et al, 2023).
Furthermore, the consensus that may exist at the onset of a crisis can dissipate as time passes (Montpetit, 2011). When a problem requires instrumental learning, policy makers usually turn to trusted experts who enjoy some form of government certification (Dunlop, 2014). Some researchers have shown how certified experts have exerted significant influence at the start of the pandemic, at the expense of other experts (Cairney, 2021; Cairney and Toth, 2023). In some places, however, these experts were quickly challenged. In Sweden, for example, experts who felt their perspective was being ignored by the government publicly disagreed with the approach proposed by the public health agency, the source of certified expertise in that country (Vetenskapsforum COVID-19, 2023; Lemor et al, 2024). Such disagreements between experts can reduce their influence and benefit other inputs, such as public opinion.
The power of worst-case scenarios
Given that scientific projections are uncertain, they often lead to multiple scenarios, ranging from the most optimistic to the most pessimistic (Pearce, 2020; Rhodes and Lancaster, 2020). Experts from the Intergovernmental Panel on Climate Change (IPCC), for example, estimate temperature increases based on various CO2 emission levels, because of the uncertainty surrounding future emissions. Different assumptions on emission levels thus lead to various projections, some optimistic and others more pessimistic (Pachauri et al, 2015). Considering these looming uncertainties, cautious climate modellers might opt to give weight to the worst-case scenario (Brister et al, 2021).
Uncertainties, coupled with the fear of losses, tend to elicit profound human reactions (Kahneman and Tversky, 1979; Kahneman, 2011), making worst-case scenarios more powerful than optimistic ones. Research has also shown that framing issues negatively bolsters public policy support (Avdagic and Savage, 2021). Hausfather and Peters (2020) explain that modellers may thus lean towards the worst-case scenario, even when they acknowledge its low likelihood, because it maximises impact. During the COVID-19 pandemic, a grim scientific projection from Imperial College is believed to have expedited initial lockdowns in many countries, although it was later deemed to have been poorly estimated (James et al, 2021). Worst-case scenarios were indeed influential early in the pandemic of COVID-19, despite knowledge of the limitations of scientific projections (Pearce, 2020).
Human cognition plays a pivotal role here, interpreting threats in ways that trigger feelings of fear, which in turn focus attention and elicit protective reactions (for example, Caplin and Eliaz, 2003; Kahneman, 2011; Maor, 2020; Martel-Morin and Lachapelle, 2022). The admission of the Finnish prime minister of having closed schools ‘out of fear’ provides a good illustration (Maor and Howlett, 2020). The fear caused by worst-case scenarios among the public can, in turn, foster momentum for reassuring public policies (Maor et al, 2020). This dynamic frequently underpins sudden and bold policy changes (Jones and Baumgartner, 2005; Shafi and Mallinson, 2023), a phenomenon observed in many health crises (Wilkinson et al, 2010). In other words, the literature shows that both scientific modellers and policy makers, in many circumstances, are led to prioritise worst-case scenarios over more optimistic scenarios.
The double-edged sword of catastrophic projections
Worst-case scenarios and their inherent negativity can have deleterious effects in the long run (Martel-Morin and Lachapelle, 2022). Also having to deal with uncertainty, the public and interest groups might be receptive to the direst experts’ projections early in a crisis, even if these projections mandate sacrifices on their part (Kreps and Kriner, 2020). They might in fact be willing to bear a substantial cost to prevent foreseen disasters, if convinced the endeavour is worthwhile (Zografakis et al, 2010). This willingness, however, might not endure.
After experiencing a crisis and shouldering the necessary sacrifices, individuals could acclimatise to the new reality to the extent they become less responsive to repeated catastrophic projections (Martel-Morin and Lachapelle, 2022), especially when these forecasts become controversial in a polarised political climate (Kreps and Kriner, 2020). As the public gradually becomes desensitised to a crisis, there may emerge a sentiment that scientists are overstating risks (Borick and Rabe, 2017). Persistent catastrophic projections, in light of prolonged collective sacrifices, might have a demoralising effect, fostering fatalism. The public may grow sceptical towards scientific projections, even if they originate from credible sources (Hausfather and Peters, 2020). They could become increasingly reluctant to bear the cost of policies – whose impacts are slow to alleviate the crisis – and become more critical of policy makers who continue to take experts seriously (Mayer and Smith, 2019). As seen during the COVID-19 pandemic, trust in scientists can wane over time (Algan et al, 2021).
The longer the crisis drags on, the more policy makers will grapple with deciding whether experts or the public should inform their policy decisions. During the pandemic, the relation between policy makers and experts indeed became, over time, increasingly contentious and experts’ influence diminished (Armingeon and Sager, 2022; Easton et al, 2022; Eichenberger et al, 2023; Zaki et al, 2023). In a democratic setting, elected officials cannot wholly distance themselves from public opinion and other interests. And as the public becomes distrustful of scientific advice, the influence of experts on policies to overcome a crisis may diminish over time.
In the field of climate change, projections have prompted the public to accept the sacrifices involved in policies aimed at keeping temperatures below a threshold beyond which, according to experts, catastrophes will occur. These grim climate change forecasts can also instil a sense of fatalism and powerlessness within the public, stemming from a sentiment that the worst is, in any case, inevitable (Lertzman, 2015). Thus, even when confronted with compelling evidence and catastrophic projections, many individuals might resort to denial, seeking solace in the belief that individual sacrifices are incapable of addressing the problem (Norgaard, 2011). Some researchers indeed highlight that the current communication strategy by experts on climate change, which often emphasises impending disasters, might not be the most effective in motivating action (Lertzman, 2015; Martel-Morin and Lachapelle, 2022).
Hypotheses about the influence of scientific projections over time
This study investigates two related hypotheses regarding the influence of experts and their projections during the COVID-19 pandemic. Our first hypothesis suggests that expert projections were initially influential, echoing findings from other health crises (Salajan et al, 2020), with this influence being contingent on public support. Specifically, we hypothesise that expert projections during the pandemic informed the stringency of non-pharmaceutical interventions (NPIs) to the extent that the public supported them. The second hypothesis explores the idea that the impact of experts’ catastrophic projections on public policy declines over time. We thus hypothesise that the longer the COVID-19 crisis persisted, the less support there was for NPIs, and the less expert projections informed decisions.
In a democratic setting, policy makers must maintain public support and if the public loses faith in expertise, the influence of experts declines. The challenge comes from catastrophism, which appeals to scientific modellers and provokes rapid political reactions. In the short term, reactions take the form of policies, often bold ones, that impose major sacrifices on the public and groups, which accept them for a period. In the longer term, however, reactions to catastrophism take the form of fatalism, which feeds distrust and denial of expert advice. The influence of scientific experts then diminishes. We formulated these hypotheses in response to recent calls for a more comprehensive understanding of how experts influence public policy (Christensen, 2021).
The existing literature on crisis management, pandemics in particular, has not fully examined the relative influence of experts in time. Studies examine the role of epidemiological data, of hospital capacities, of trust, of inter-country diffusion, and of policy makers’ attitudes towards the rapidity or stringency of policies, but none have explored the influence of experts, in relation to other forces, throughout the course of a public health crisis (Sebhatu et al, 2020; Forster and Heinzel, 2021; Toshkov et al, 2021; Bourdin et al, 2022; Jalloh et al, 2022; Wang et al, 2024). Eichenberger et al (2023) comes closest to doing so but fail to examine the relationship between expert influence and public support.
Our study employs a dual database approach, utilising mixed methods to analyse both quantitative and qualitative data. On one hand, it includes expert hospitalisation projections, data on interest groups, public adherence and public opinion (Lemor et al, 2023). On the other, it encompasses all transcriptions of press conferences held by decision makers in Quebec, Canada, throughout the pandemic (Lemor, 2024). This comprehensive methodology allows for an in-depth analysis that highlights the diminishing influence of experts over time in relation to other factors that matter in a democratic setting.
Methodology and data
The COVID-19 crisis provides an excellent opportunity to test our hypotheses. First, infectious diseases like COVID-19, akin to climate change, call for scientific modelling, as seen through widespread epidemiological projections reported in the media during the pandemic’s early stages (Ford, 2020). Second, given the contagiousness of SARS-CoV-2 and the virulence of its induced disease for a significant portion of the population, epidemiologists and mathematicians can produce projections that can be construed as catastrophic without jeopardising their credibility, just as modelling scientists can do it for climate change. Third, the shorter duration of the COVID-19 crisis compared to climate change offers an opportunity for continuous observation from beginning to end. Finally, the rapid and successive case increases and decreases during the COVID-19 crisis provide an opportunity to examine causal relationships between scientific expertise, public opinion and policy, a more challenging task in climate studies due to slower variations over time, notably in projections.
Quebec as a case study
Quebec, a Canadian province, represents an exemplary case for examining our hypothesis due to its strategic use of NPIs throughout the pandemic, such as lockdowns and venue closures (Han and Breton, 2022). Provincial authorities in Canada had the responsibility for NPIs, except for managing travel restrictions. Acknowledging its reliance on epidemiological projections, the Quebec government began publicly releasing these projections from the second wave in September 2020. Our research incorporates public opinion and adherence data from March 2020 to February 2022 and extensive qualitative analysis from press conference transcripts, providing insight into policy making during the initial and later pandemic waves (Lemor et al, 2023; Lemor, 2024). Despite the unavailability of hospitalisation projections in the first wave, our data set remains relatively comprehensive, covering this crisis extensively.
Epidemiological projections
The epidemiological projections employed in our study were produced by the National Institute for Excellence in Health and Social Services (INESSS) at the request of the Quebec government. These projections, based on a statistical model, forecasted weekly hospitalisations in both regular and intensive care units. While the Quebec government initially used informal epidemiological insights, INESSS was formally commissioned to produce weekly projections from the pandemic’s second wave, with data being public from September 2020. These projections, which incorporated cumulative infection numbers and vaccination counts, led us to use death-count data in our model to avoid multicollinearity. Notably, unlike climate change forecasts, these epidemiological projections exhibited significant short-term fluctuations, reflecting the various stages and severity of the pandemic. This variability was critical for assessing the influence of epidemiology on policy decisions, particularly how projections of increased hospitalisations might lead to more stringent NPIs.
Policy
Our second hypothesis suggests that catastrophic expert projections initially influenced public policies, but gradually lost their influence over time. During the pandemic, NPIs had been closely monitored and quantified. The Oxford Coronavirus Government Response Tracker’s method for assessing policy stringency, adapted by the Institute for Research on Public Policy (IRPP) for the Canadian context, serves as a basis for our analysis (Hale et al, 2021; IRPP, 2021; 2022). We use the IRPP’s stringency index, which rates policy stringency on a scale from 0 to 100, based on 12 NPIs like curfews, mask mandates and closures of various venues. A weekly average of this index was created to ensure consistency in our analysis.
Time and public support
According to our theory, the diminishing influence of scientific modellers over time is closely tied to public support. As time progresses, the public tends to become less responsive to experts’ catastrophic projections and, consequently, less supportive of policies based on these forecasts. To measure this, we utilise Quebec government-mandated surveys that assess public support for actual NPIs (Gouvernement du Québec, 2020). However, public opinion on policies is known to fluctuate thermostatically, dissatisfaction leading to policy adjustments, which then alter public satisfaction levels (Soroka and Wlezien, 2009). This dynamic is particularly evident during a pandemic, where the strictness of NPIs may gradually erode public support, nudging the government towards more permissive measures. In turn, more permissiveness can either restore or boost public support levels, making it difficult for the government to determine, based on opinion data, whether the public would be likely to accept greater NPI stringency in the face of a situation that would justify it.
Therefore, we consider public adherence as an additional indicator. We use an adherence index from the Quebec National Institute of Public Health (INSPQ), which is based on surveys evaluating public attitudes towards general public health measures like hand hygiene, physical distancing and participating in large gatherings (INSPQ, 2022). Negative attitudes towards these measures, indicative of low adherence, suggest to governments that intensifying NPIs might negatively impact public support, irrespective of what expert projections might indicate about potential hospitalisations.
Interest groups
Interest-group activity might compete with expert projections as an input into decisions on NPIs’ stringency, as shown in studies on the influence of interest groups on regulatory decisions (Beaulieu-Guay et al, 2021). Early in a crisis, uncertainty may lead groups to cautiously consider expert projections, even if the associated policies adversely affect their interests. Over time, however, as the burden of stringent policies intensifies, these groups might increasingly pressure governments to lessen NPI stringency, regardless of whether projections suggest maintaining it to avoid hospital overloads.
For our analysis, we assess interest-group activity through media content, using a comprehensive Canadian media database (Rocheleau, 2017). We focus on articles from Quebec newspapers related to COVID-19, specifically targeting four sectors most affected by NPIs: restaurants, cultural venues, retail businesses and non-essential services. Out of the 12,687 articles gathered about these groups, we randomly selected 1,630 for manual coding to identify mentions of interest-group activity. We further distinguished articles indicating dissatisfaction with Quebec’s COVID-19 NPIs. We subsequently computed a monthly average of articles conveying the dissatisfaction of interest groups in the four sectors.
Empirical test and methodology overview
Our study utilises a comprehensive database to empirically examine the trade-off that emerges over time between expert modellers’ influence on policy and that of the public and groups. This database includes qualitative data from press conferences since the pandemic’s beginning (Lemor, 2024) and quantitative data on NPIs’ stringency, public support, hospitalisation projections, public adherence to health measures, and interest-group activity, all indexed or scaled out of 100 and updated weekly (Lemor et al, 2023).
We first conduct a descriptive analysis to observe data trends supported by qualitative analysis. We then perform an OLS regression to examine the impact of expert projections on policy stringency while accounting for mortality and public adherence. We finally incorporate into the model an interaction between expert projections and public adherence to gauge the extent to which expert projections are contingent on public support. This approach enables estimations of the extent to which expert projections had influence, as well as the extent to which public support mediates their impact on policy over time.
Results
- Hypothesis 1:Expert projections during the pandemic informed the stringency of NPIs, to the extent that the public supported them.
Descriptive and qualitative analyses
The Quebec government implemented strict NPIs as early as 12 March 2020, to ‘flatten the curve’ of COVID-19 contagion, as stated by the National Director of Public Health. Beyond the suite of measures announced on 12 March to reduce transmission, a health emergency decree was issued on 13 March. Our qualitative analysis indicates that these initial policies were shaped by experts predicting an imminent disaster if the government opted for a tepid response.
For instance, on 12 March, the National Director of Public Health referenced projections (see Appendix, Table 1, citation 1) including alarming contagion scenarios (see Appendix, Table 1, citation 3), while acknowledging their uncertainty (see Appendix, Table 1, citations 2 and 3). He also mentioned public fears (see Appendix, Table 1, citation 4) and suggested that this justified serious consideration of the bleakest projections and the adoption of particularly strict policies (see Appendix, Table 1, citation 5). The analysis of press conferences from the first wave thus confirms that catastrophic projections by experts exerted significant influence during the crisis’s early stages, both on the public and governmental authorities.
Our quantitative data, starting from the second wave, are presented in Figure 1 and further bolster our hypothesis. While the top graph shows raw data, the bottom graph presents smoothed data. The descriptive data suggest that stringency varied along expert projections during the early waves of the crisis. Following a slowdown in the summer of 2020, INESSS experts anticipated a new disaster in the autumn, predicting a peak of nearly 900 hospitalisations in December. These projections have given rise to serious concerns over Quebec’s limited hospital capacity compared to other jurisdictions (Corniou, 2020). Throughout the pandemic, policy makers endeavoured to bolster this capacity by instructing hospitals to defer less urgent surgeries. In December 2020, hospitals were advised to discharge 50 per cent of surgical patients, postponing numerous cancer treatments in anticipation of projected COVID-19 hospitalisations (Lacoursière and Jean, 2020; Radio-Canada, 2022). Unsurprisingly, the successive expert projections of autumn 2020 were followed by incremental escalations in the stringency of NPIs, lasting through to January 2021. The index peaked in early 2021 following the government’s decision to implement a curfew, a unique move in North America. Intriguingly, this heightened stringency did not undermine public support for the government’s crisis approach.
Distribution of descriptive data over time
Citation: Policy & Politics 2025; 10.1332/03055736Y2024D000000064
While our data indicates that interest groups were relatively prominent in the press at the onset of the second wave, Figure 1 illustrates their seldom criticism of the government, contrary to subsequent waves. Consequently, there was a prevailing consensus both from the public and interest groups concerning health measures and the projections informing them. This early crisis consensus mirrors findings in various opinion surveys conducted in Quebec during this period (Leger, 2023).
Hospitalisation forecasts began waning in January 2021 and persisted at comparatively lower levels during Waves 3 and 4, relative to their previous levels. In alignment with expert projections, the stringency of NPIs had markedly subsided by June 2021 and remained at relatively low levels during Wave 4, a wave wherein the projections were least worrisome. Unsurprisingly, public support remained robust throughout Waves 3 and 4.
Quantitative analysis
To further investigate our hypothesis, we employed a regression model (Table 1) and a projection plot to determine whether expert projections, moderated by public support, influenced public policies. In the OLS analysis, projections concerning future COVID-19 hospitalisations (variable ‘Projections’) were statistically significant, indicating a negative association with the stringency of NPIs (coefficient = −0.302, p < .01). Although this relationship might seem counterintuitive, it aligns with our hypothesis. The interaction term (Projections * Adherence, coefficient = 0.011, p < .01) reveals that expert projections do increase the stringency of NPIs, but only to the extent that the public adheres to health measures. The negative coefficient for the variable Projections alone is explained by low adherence during certain stages of the crisis. In summary, the relationship between projections and stringency is mediated by public adherence to health measures.
Effects of experts’ projections and adherence (including interaction effects) on policy stringency
| OLS 1 | |
|---|---|
| (Intercept) | −7.075 (3.377)* |
| Stringency −1 | 0.617 (0.060)*** |
| Death | −0.015 (0.080) |
| Projections | −0.302 (0.106)** |
| Adherence | 0.632 (0.125)*** |
| Projections * adherence | 0.011 (0.004)** |
| Num. Obs. | 80 |
| R2 | 0.967 |
| R2 Adj. | 0.964 |
| AIC | 431.2 |
| BIC | 447.9 |
| Log.Lik. | 429.802 |
| F | 3.28 |
Notes: + p < .1, * p < .05, ** p < .01, *** p < .001.
The population’s adherence level to the NPIs (variable ‘Adherence’) has a significant positive coefficient of 0.632 (p < .001), highlighting its substantial role in determining stringency. The variable measuring deaths from COVID-19 is not statistically significant in this model.1 With an adjusted R^2 of 0.967, the model explains a large part of the variation in the stringency of NPIs.
In addition to the regression table, we produce an interaction plot to visualise the relationship between Projections and Adherence, as estimated by our model (Figure 2). The lines of the graph represent three levels of public adherence to NPIs. The interaction graph facilitates the visualisation of the relationship between hospitalisation projections and NPIs’ stringency and highlights differences according to adherence levels. For instance, if a line displays a steeper gradient at a higher adherence level, it suggests that when the public strongly adheres to the measures, hospitalisation projections have a more pronounced effect on stringency.
Effects of experts’ projections on stringency by adherence levels (minimum, median, maximum)
Citation: Policy & Politics 2025; 10.1332/03055736Y2024D000000064
Visualising these predictions in the interaction plot, several patterns emerge. For the minimal adherence level, the relationship between hospitalisation projections and the severity of health measures is negative, indicating that low adherence is associated with a decrease in policy stringency, even as the projected hospitalisations increase. At the median adherence level, the relationship becomes positive, suggesting that moderate adherence is related to an increase in policy stringency when projected hospitalisations increase. At the maximum adherence level, the slope is markedly positive, indicating that a very high level of adherence produces a substantial increase in policy stringency, even with modest increases in the projected hospitalisations.
- Hypothesis 2:The longer the COVID-19 crisis persisted, the less support there were for NPIs, and the less expert projections informed decisions.
Overall analysis
The emergence of the Omicron variant in late November 2021 altered the course of the pandemic. Towards the end of the fourth wave, before Omicron’s detection, the Quebec government adopted a reassuring stance. Bolstered by favourable projections in October and an exceptional vaccination coverage, the government committed to relaxing restrictions on gatherings for the holiday season, enabling family reunions (see Appendix, Table 1, references 8 and 9). The National Director of Public Health acknowledged then the effort of the population and cautioned against a potential descent into public fatalism if the stringency of NPIs remained high (see Appendix, Table 1, references 10 and 11). However, this optimism was excessive in the face of an ever-evolving virus. Indeed, the arrival of the of Omicron variant brought back catastrophic projections. Omicron being considerably more contagious than previous variants, INESSS forecasts predicted deadly hospital saturation were NPIs’ stringency to remain at the level of the fourth wave. As illustrated in Figure 1, INESSS projected hospitalisations to peak at 2,045 by early January 2022, the highest level since the pandemic’s onset, despite high vaccination coverage. Two years into the crisis, these projections were particularly alarming, not only due to the unprecedented number of hospitalisations, but also because healthcare capacity continued to be a challenge (Lacoursière and Chouinard, 2021).
The January 2022 projections prompted the government to retract its earlier announcement regarding holiday-season gatherings, in addition to boosting the overall stringency of NPIs. The string of announcements culminated with the reinstatement of the curfew. The grim forecasts from INESSS once again wielded significant influence on governmental policy at the start of the fifth wave. This time, however, public support began to decline, while interest groups responded unfavourably to the increase in stringency (Figure 1).
Public support reached a low point in December 2021. Interestingly, it rebounded in February 2022, only after the government’s announcements of the end of the curfew and the gradual relaxations in NPIs’ stringency. The adherence index, a less thermostatic gauge of public support, also began a decline in December 2021 and consistently decreased until the end of the observation period (Figure 1). Interest groups vocally aired their grievances and discontent. While 49 per cent of the articles citing interest-group activity indicated policy dissatisfaction during the pandemic’s second wave, dissatisfaction surged to 61 per cent during the fifth wave. Policy makers did not conceal their sensitivity to public discontent. Premier François Legault, expressing his concerns about hospitals, mentioned the public’s growing exasperation (see Appendix, Table 1, reference 12). He further suggested that he had to start caring for ‘social cohesion’ and ‘peace’ (see Appendix, Table 1, reference 13). On 8 February 2022, responding to a journalist’s query, the premier hinted that reverting to strict social distancing policy was now off the table, regardless of projections.
Nonetheless, according to the INESSS projections shown in Figure 1, the crisis was far from over. A sixth wave was announced in April 2022, with hospitalisation forecasts approaching those of the fifth wave. Yet, this time, the government faced a challenging decision as public adherence figures signalled a reluctance to support further escalations in NPIs stringency (Figure 1). Indeed, expert projections were met with scepticism and fatalism by the public. Surveys, for instance, indicated that at this stage, most of the population perceived the crisis as definitively over, marking the public’s widespread fatalism (Leger, 2022: 15). For the first time since the pandemic’s onset, grim forecasts failed to drive any increase in the stringency of NPIs.
The lower graph in Figure 1 shows that at the crisis’s onset (see references 6 and 7, Appendix, Table 1), the public was deferential to experts’ catastrophic predictions. The population accepted the sacrifices deemed necessary to prevent a disaster in the hospitals. However, after enduring significant sacrifices over a long period and becoming accustomed to living amid the threat of hospital overcrowding, the public became increasingly defiant of experts’ scientific projections that called for increases in NPIs stringency (Figure 1). The same was true for interest groups. Less active during the early stages of the crisis despite the costs imposed on them by severe public health measures, they became more reactive to increases in policy stringency as the crisis prolonged. Individuals and groups alike were gradually acclimatising to the crisis, becoming more fatalistic towards alarming projections. The willingness to accept policies based on these forecasts hence waned, as did the expert modellers’ influence on policy.
Conclusion
This article examines two hypotheses about the influence of scientific projections during a crisis and within a democratic context. While we have shown that catastrophic projections can significantly impact policy decisions at the onset of a crisis, we have also shown that the influence of such projections wanes as time passes. As a crisis starts to feel interminable, public and interest-group support for costly preventive measures wanes, along with the influence of experts advocating bold policy decisions. Over time, groups become increasingly contentious and the public more critical of policy makers. Accountable to the public, a democratic government in such circumstances may gradually cease to rely on alarming scientific projections to inform its policies.
This article contributes to the theorisation of the influence of scientific expertise in policy making, particularly in crisis situations (Christensen, 2021). It highlights a specific category of expertise, scientific projection, which is useful when scientific information is required quickly, given the urgency of a situation (Sarkki et al, 2014). Right from the start of the pandemic, policy makers were able to rely on modellers who were ready to provide useful information for decision making. Projections are also a type of scientific information that lends itself to catastrophism, a position that generally does not inspire indifference when its source is credible. Indeed, we have shown that scientists capable of producing catastrophic projections are likely to have a significant influence on decisions at the start of a crisis. Finally, scientific expertise – even that of modellers, tailored to the needs of decision makers – does not have a constant effect over time. Indeed, scientific experts who succeed in convincing policy makers to take bold decisions, at great cost to the population, may end up losing all influence (Hausfather and Peters, 2020). When faced with the choice of which of the experts or the public should inform their decisions, governments in democratic countries generally prefer the latter (Soroka and Wlezien, 2009).
It is also worth noting that expert projections are rarely monolithic. In most crises, including the COVID-19 pandemic, multiple groups of experts may produce differing or even conflicting projections. For example, during the pandemic, policy makers had access to various projections – some more optimistic, others more catastrophic – which probably influenced their decision making differently at different stages of the crisis. While this study focuses on projections requested by the government from one of its agencies, the availability of multiple expert projections is likely to add complexity to how scientific advice is received by policy makers (Montpetit, 2011). This plurality of projections could, in turn, affect the dynamics of influence over time and warrants further examination in future studies.
This study has some limitations worth underlining. First, our single-case study approach advises caution in generalising. While the description of the Quebec COVID-19 crisis we provide here shares many common elements with those of other democratic countries, it also has a few peculiarities. In particular, the strong support of the Quebec public for health measures lasted longer than elsewhere, which may suggest singular attitudes in the province towards expertise. Second, we lack time-series public attitude data explicitly related fatalism during the COVID-19 crisis. Although we argue that catastrophist projections feed public fatalism over time, we have no direct observation of this dynamic. This is important, because fatalism plays a key role in the decline in public support for NPIs, which is the causal mechanism for the erosion of modellers’ influence. Similarly, we do not compare different levels of catastrophism, preventing us from examining its precise role in the causal relationship. Third, our media analysis does not consider the media’s influence on public opinion, although a recent study suggests that public opinion precedes media coverage (Wlezien, 2023). Lastly, our empirical study does not dwell on some notions that appear in our theoretical reasoning. The eventual abandonment of expert advice during a crisis could be the result of a learning process, as indicated in our theoretical section (Dunlop, 2014). For the purposes of this study, however, we did not consider it necessary to use learning indicators. We leave these tasks for future research on the influence of scientific experts.
This article lays the groundwork for research in public policy, as well as having normative implications in fields as diverse as climate change, political psychology, communication and crisis management. While much needs to be done to fully demonstrate the effect of catastrophism over time, our findings nonetheless advise caution. Policy makers, modellers, crisis managers and communicators should be aware that catastrophism, if initially powerful, can also become counterproductive over time.
Note
Other controls such as the number of cases, the number of vaccinated individuals or the number of hospitalisations could have been used. However, several reasons prevent the inclusion of these controls. First, the models used by INESSS to produce hospitalisation projections utilised the number of cases, which precludes the use of the number of cases as a control due to multicollinearity. Second, these models also consider the number of vaccinated individuals, which also prevents their inclusion as a control for the same reason. Finally, hospitalisations present significant challenges because it remains impossible to determine how many hospitalisations on which date influence the severity of measures. Indeed, hospitalisations cannot be reliably used as a predictor of severity in the following weeks, as an increase in hospitalisations indicates that contamination has already occurred.
Funding
This work was supported by the Social Sciences and Humanities Research Council (SSHRC) of Canada [grant number 435 2021 0332] and the Fonds de recherche du Québec (FRQ) [grant number 203845].
Acknowledgements
Authors would like to thank the Centre Interuniversitaire de Recherche sur la Science et la Technologie (CIRST) for their valuable assistance and collaboration in writing this article. Olivier Santerre deserves specific thanks for his precious help and insights.
AI declaration
This research article was developed independently by the author, with AI assistance limited to facilitating the writing of code and translating the text from French to English using ChatGPT.
Conflict of interests
The authors declare that there is no conflict of interest.
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