Unintended consequences of communicating rapid COVID-19 vaccine policy changes– a qualitative study of health policy communication in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The success of the COVID-19 vaccination roll-out depended on clear policy communication and guidance to promote and facilitate vaccine uptake. The rapidly evolving pandemic circumstances led to many vaccine policy amendments. The impact of changing policy on effective vaccine communication and its influence in terms of societal response to vaccine promotion are underexplored; this qualitative research addresses that gap within the extant literature. METHODS: Policy communicators and community leaders from urban and rural Ontario participated in semi-structured interviews (N = 29) to explore their experiences of COVID-19 vaccine policy communication. Thematic analysis was used to produce representative themes. RESULTS: Analysis showed rapidly changing policy was a barrier to smooth communication and COVID-19 vaccine roll-out. Continual amendments had unintended consequences, stimulating confusion, disrupting community outreach efforts and interrupting vaccine implementation. Policy changes were most disruptive to logistical planning and community engagement work, including community outreach, communicating eligibility criteria, and providing translated vaccine information to diverse communities. CONCLUSIONS: Vaccine policy changes that allow for prioritized access can have the unintended consequence of limiting communities' access to information that supports decision making. Rapidly evolving circumstances require a balance between adjusting policy and maintaining simple, consistent public health messages that can readily be translated into action. Information access is a factor in health inequality that needs addressing alongside access to vaccines.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".