Examining the impact of healthcare sector Covid-19 policies on access to care in Ontario, Canada: A mixed methods study
Notice bibliographique
Résumé
Statement of the problem: In December of 2020, and upon an FDA Emergency Use Authorization of two mRNA-Covid-19 vaccines, a vaccination campaign was launched in most hospitals across Canada. Vaccine products were tested for safety and efficacy in large, albeit short-term, RCTs, that included mostly young and healthy adults. Potential safety signals, many of them long term, would become apparent over time – through data from early adopters such as Israel and India, or reporting systems across the world, such as the United Kingdom (UK) Yellow Card, the World Health Organization VigiBase, or the United States (US) Centers for Disease Control (CDC) Vaccine Adverse Events Reporting System (VAERS). These signals were concerning. For instance, already in January 2021, VAERS would record a higher number of deaths post-Covid vaccination than any other vaccine since the system was established in 1990. By mid-2021, the number of recorded deaths post-Covid-19 vaccination would surpass the total number of deaths post all vaccines deployed in the preceding 30 years. Many healthcare workers with firsthand experience in their day-to-day practice of vaccine harms on patients or colleagues, and/or access to data from Israel, India, or systems such as VAERS, were cognizant of signals of harm not included in the hospital-provided vaccine information literature. Anecdotal evidence indicates that as a result, many chose not to get vaccinated despite job loss being the cost of such choice. Many others opted for early retirement or career change. Yet others became disabled or lost their lives, as indicated by government and actuarial data from the UK and the USA. The type and scope of the impact of the policy of vaccine mandates on the Canadian health workforce, however, remains unknown. Given the ongoing healthcare labor force shortage affecting Canadians, documenting, and explaining what one major contributor may be is critical. Our project assesses this potential contributor through a mixed-method study of the province of Ontario, Canada. Research question: Our main research question is: “What has been the impact of healthcare sector Covid-19 policies on access to care in Ontario, Canada?” Ancillary questions include: “What is known from publicly accessible governmental data (e.g., Ontario Health’s databases) and Freedom of Information Requests (FOIR) from government and hospitals, about the impact of the Covid-19 vaccine mandates on the healthcare labour force, specifically staff reductions? What is known about the implications of this impact for access to care in the province? What is the rationale informing the current policy of ongoing vaccine mandates for new and existing staff? What are the views and lived experience of Canadian health workers concerning the crisis in the health labour force? Aims: Our study aims to document and explain an under researched aspect of the drivers of the health crisis, specifically the impact of healthcare sector Covid-19 policies, on access to care in Ontario. Methods: To address our research questions and achieve our study aim, we will use Creswell (2015) mixed methods approach to inform our narrative literature review, policy analysis, FOIA investigation, survey of healthcare workers, and interview of a sample of workers from within survey respondents. Significance: The Covid-19 crisis has resulted in a severe understaffing of hospitals across Ontario, compounding the problems of lack of experienced health workers and staff burnout. To the best of our knowledge, there is no research into what led to the policy decision to terminate experienced, healthy unvaccinated health workers, a decision ongoing to this day. By summarizing extant evidence, identifying information gaps, surveying the health workforce, and gaining a deeper understanding of the lived experience within this workforce our research should contribute to more effective and equitable policies in the health sector moving forward, relevant not only to the province of Ontario but potentially throughout Canada.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,006 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 tête enseignante, 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 ».