Beliefs associated with Intentions of Non-Physician Healthcare Workers to Receive the COVID-19 Vaccine in Ontario, Canada
Bibliographic record
Abstract
Abstract Achieving herd immunity of SARS-CoV-2 through vaccines will require a concerted effort to understand and address barriers to vaccine uptake. We conducted a web-based survey of non-physician HCWs, informed by the Theoretical Domains Framework, measuring intention to vaccinate, beliefs and sources of influence relating to the COVID-19 vaccines, and sociodemographic characteristics. Vaccination non-intent was associated with beliefs that vaccination was not required because of good health, lower confidence that the COVID-19 vaccine would protect their family and patients, and that getting vaccinated was a professional responsibility. Vaccination non-intent was strongly associated with mistrust about how fast the vaccines were developed and vaccine safety concerns. Communication directed at non-physician HCWs should be tailored by ethnic subgroups and settings to increase salience. Messaging should leverage emotions (e.g., pride, hope, fear) to capture interest, while addressing safety concerns and confirming the low risk of side effects in contrast to the substantial morbidity and mortality of COVID-19. Emergent data about reduced transmission post-vaccination will be helpful.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".