Effects of the COVID-19 Vaccine Mandate on Healthcare Workers’ Decisions to Refuse Vaccination and Quit Their Jobs from Canadian Hospitals
Bibliographic record
Abstract
COVID-19 vaccine uptake and compliance implementation challenges exist among some Canadian healthcare workers (HCWs), including hospital administrators, despite free vaccination as a preventive measure to control the spread. Earlier studies have examined COVID-19 vaccine hesitancy and refusal among Canadian HCWs, but not how the -19 vaccine and vaccine mandates may have influenced their decisions to refuse vaccination and quit their jobs. This qualitative phenomenological study involved exploring how Canadian hospital HCWs’ lived experiences with the COVID-19 vaccine and vaccine mandates affected their decisions to refuse COVID-19 vaccination and quit their jobs. The theory of reasoned action was used to guide interview questions to understand this topic. I recruited for Zoom interviews using both the online crowdsourcing Amazon Mechanical Turk (Mturk) platform and snowball sampling. All participants were Canadian HCWs who worked in a hospital with a COVID-19 vaccine mandate policy, between 20 and 60 years, possessed a Mturk verification ID, refused the COVID-19 vaccination, and quit their job due to vaccine mandate policies. Transcribed interviews were coded and analyzed using Quirkos thematic analysis with the following themes: safety, skepticism towards vaccine efficacy, newness of the vaccine, strain variability, public image, uncertainty, autonomy, and personal beliefs against mandated health interventions. These findings may help address ethical dimensions that are involved in mandatory vaccination policies and the importance of respecting individual autonomy and personal medical choices.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".