Trust Dynamics and Equity in Public Health in Canada: Protocol for a Mixed Methods Project in the Postpandemic Era (Preprint)
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,081 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,004 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,005 | 0,009 |
| Études des sciences et des technologies | 0,011 | 0,004 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,074 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».