Strategies and resources used by public health units to encourage COVID-19 vaccination among priority groups: a behavioural science-informed review of three urban centres in Canada
Bibliographic record
Abstract
BACKGROUND: Ensuring widespread COVID-19 vaccine uptake is a public health priority in Canada and globally, particularly within communities that exhibit lower uptake rates and are at a higher risk of infection. Public health units (PHUs) have leveraged many resources to promote the uptake of recommended COVID-19 vaccine doses. Understanding barriers and facilitators to vaccine uptake, and which strategies/resources have been used to address them to date, may help identify areas where further support could be provided. We sought to identify the strategies/resources used by PHUs to promote the uptake of the first and third doses of the COVID-19 vaccine among priority groups in their jurisdictions. We examined the alignment of these existing strategies/resources with behavioral science principles, to inform potential complementary strategies/resources. METHODS: We reviewed the online and in-person strategies/resources used by three PHUs in Ontario, Canada to promote COVID-19 vaccine uptake among priority groups (Black and Eastern European populations, and/or neighbourhoods with low vaccine uptake or socioeconomic status). Strategies/resources were identified from PHU websites, social media, and PHU liaison. We used the Behaviour Change Techniques (BCT) Taxonomy - which describes 93 different ways of supporting behaviour change - to categorise the types of strategies/resources used, and the Theoretical Domains Framework - which synthesises 14 factors that can be barriers or facilitators to decisions and actions - to categorise the barriers and facilitators addressed by strategies/resources. RESULTS: PHUs operationalised 21 out of 93 BCTs, ranging from 15 to 20 BCTs per PHU. The most frequently operationalised BCTs were found in strategies/resources that provided information about COVID-19 infection and vaccines, increased access to COVID-19 vaccination, and integrated social supports such as community ambassadors and engagement sessions with healthcare professionals. Identified BCTs aligned most frequently with addressing barriers and facilitators related to Knowledge, Environmental context and resources, and Beliefs about consequences domains. CONCLUSION: PHUs have used several BCTs to address different barriers and facilitators to COVID-19 vaccine uptake for priority groups. Opportunities should be pursued to broaden the scope of BCTs used (e.g., operationalizing the pros and cons BCT) and barriers/facilitators addressed in strategies/resources for ongoing and future COVID-19 vaccine uptake efforts among general and prioritised populations.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.029 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".