Examining the impact of healthcare sector Covid-19 policies on access to care in Ontario, Canada: A mixed methods study
Bibliographic record
Abstract
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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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".