Barriers and facilitators to COVID-19 vaccine acceptability among people incarcerated in Canadian federal prisons: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: Canadian correctional institutions have been prioritized for COVID-19 vaccination given the multiple outbreaks that have occurred since the start of the pandemic. Given historically low vaccine uptake, we aimed to explore barriers and facilitators to COVID-19 vaccination acceptability among people incarcerated in federal prisons. METHODS: Three federal prisons in Quebec, Ontario, and British Columbia (Canada) were chosen based on previously low influenza vaccine uptake among those incarcerated. Using a qualitative design, semi-structured interviews were conducted with a diverse sample (gender, age, and ethnicity) of incarcerated people. An inductive-deductive analysis of audio-recorded interview transcripts was conducted to identify and categorize barriers and facilitators within the Theoretical Domains Framework (TDF). RESULTS: From March 22-29, 2021, a total of 15 participants (n = 5 per site; n = 5 women; median age = 43 years) were interviewed, including five First Nations people and six people from other minority groups. Eleven (73%) expressed a desire to receive a COVID-19 vaccine, including two who previously refused influenza vaccination. We identified five thematic barriers across three TDF domains: social influences (receiving strict recommendations, believing in conspiracies to harm), beliefs about consequences (believing that infection control measures will not be fully lifted, concerns with vaccine-related side effects), and knowledge (lack of vaccine-specific information), and eight thematic facilitators across five TDF domains: environmental context and resources (perceiving correctional employees as sources of outbreaks, perceiving challenges to prevention measures), social influences (receiving recommendations from trusted individuals), beliefs about consequences (seeking individual and collective protection, believing in a collective "return to normal", believing in individual privileges), knowledge (reassurance about vaccine outcomes), and emotions (having experienced COVID-19-related stress). CONCLUSIONS: Lack of information and misinformation were important barriers to COVID-19 vaccine acceptability among people incarcerated in Canadian federal prisons. This suggests that educational interventions, delivered by trusted health care providers, may improve COVID-19 vaccine uptake going forward.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".