Trust Dynamics and Equity in Public Health in Canada: Protocol for a Mixed Methods Project in the Postpandemic Era (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic impacted trust in public health, medical care, scientific, and governmental institutions; this influenced health, health-seeking behaviors, and adherence to public health measures. Understanding how trust evolves is necessary for informing future public health crisis management and strategies. Rebuilding public trust is key to pandemic preparedness worldwide. This research protocol examines these trust dynamics, their determinants, and their implications. OBJECTIVE This study aimed to investigate the evolution of public trust in scientists, public health and medical care institutions, governments, and social and personal networks during the COVID-19 pandemic. It sought to identify key factors contributing to trust maintenance or erosion, particularly in marginalized and minority communities, and understand the impact of trust level on public health adherence. METHODS A sequential, explanatory mixed methods approach was implemented, consisting of an initial quantitative survey followed by qualitative interviews. The nationwide representative survey included 5607 Canadian residents as of May 2024. The questionnaire assessed trust in 6 key trust sources before and during the pandemic: provincial and federal governments, public health authorities, medical care providers, health scientists, and social and personal networks. To complement, 41 qualitative interviews were conducted to understand participants’ lived experiences, perceptions, and how trust played into both. Data have been analyzed using quantitative statistical techniques and qualitative phenomenological analysis, and the results have been integrated to derive comprehensive insights. All phases of data collection and analysis were finalized by early 2025. The project then advanced to paper preparation, dissemination at national conferences, and knowledge-translation activities, including the report development and public-facing outputs, scheduled for completion in end-2025. RESULTS This Canadian survey included participants from all 10 provinces and 2 territories; the provinces and territories’ samples matched the proportions of each in the overall Canadian population. Age and sex or gender were well distributed across the sample. Additionally, 18.6% (1040/5607) identified as an ethnic minority, 12.7% (710/5607) identified as Indigenous, including 7.2% (403/5607) First Nations, 4.8% (270/5607) Métis, and 0.4% (25/5607) Inuit. Fifty-five percent (3094/5607, 55.2%) had received at least 1 dose and planned to stay up to date with booster recommendations, while 36.6% (2052/5607) were vaccinated but did not intend to receive additional doses. A small percentage, 0.7% (39/5607), had not yet been vaccinated but were open to it, whereas 6.4% (359/5607) had not received a vaccine and did not plan to take a vaccine. CONCLUSIONS The COVID-19 pandemic has underscored the critical role of trust in public health behavior and crisis response. This study explores how consistent, transparent communication and equity-driven policies may contribute to maintaining public trust, particularly among marginalized communities. By examining trust dynamics and identifying potential disparities, this study aims to inform evidence-based public health strategies and improve preparedness for future health emergencies. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT DERR1-10.2196/75199
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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 | 0.081 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.009 |
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".