Does healthcare access affect trust in institutions? Empirical evidence in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: The paper investigates the determinants of citizens' trust in institutions, including banks and companies, during the COVID-19 pandemic in Canada. DESIGN/METHODOLOGY/APPROACH: We used a survey, conducted in 2020, with 36,851 respondents to identify what made some trust and others distrust institutions during the COVID-19 pandemic. Our methodology incorporated ordinary least squares and logit estimations. FINDINGS: Lack of healthcare access had a significant negative impact on people's trust in institutions. Consistent with life experience theory, we found that individuals facing healthcare barriers may distrust firms and banks. Sociodemographic variables, including gender, education and marital status, were likely to shape the effect of healthcare access problems on trust in institutions. Moreover, mental health issues stemming from healthcare access problems adversely affected trust in banks and firms, suggesting that people who had mental health problems during COVID-19 were more likely to lose trust in these institutions. The relationship between healthcare access and trust in banks and firms was more pronounced among men and highly educated people. The results were robust to the instrumental variable approach. PRACTICAL IMPLICATIONS: We showed that a link between trust in institutions and problems with healthcare access can inspire partnerships between Canadian institutional entities, typically banks and firms, and healthcare organizations. This would help strengthen long-term trust in these institutions. ORIGINALITY/VALUE: The potential long-term economic consequences of COVID-19 created a crisis in the public's trust in institutions, typically firms and banks. This paper examined the relationship between healthcare access and trust in institutions, addressing the limited evidence on this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".