Resilience throughout COVID-19: Unmasking the realities of COVID-19 and vaccination facilitators, barriers, and attitudes among Black Canadians
Bibliographic record
Abstract
Black communities have suffered disproportionately higher numbers of COVID-19 cases and deaths in Canada. Recognizing the significance of supporting targeted strategies with vulnerable populations extends beyond the COVID-19 pandemic, as it addresses longstanding health disparities and promotes equitable access to healthcare. The present study investigated 1) experiences with COVID-19, 2) COVID-19's impact, and 3) factors that have influenced COVID-19 vaccine acceptance and uptake among stakeholders and partners from the Federation of Black Canadians' (FBC). We conducted semi-structured interviews with 130 individuals and four focus groups with FBC, including stakeholders and partners, between November 2021 and June 2022. The semi-structured interviews and focus group discussions were conducted virtually over Zoom and lasted about 45 minutes each. Conversations from interviews and focus groups were transcribed and coded professionally using team-based methods. Themes were developed using an inductive-deductive approach and defined through consensus. The deductive approach was based on Consolidated Framework for Implementation Research (CFIR) domains and constructs. First, regarding experiences with COVID-19, 36 codes were identified and mapped onto 13 themes. Prominent themes included 39 participants who experienced highly severe COVID-19 infections, 25 who experienced stigma, and 18 who reported long recovery times. Second, COVID-19 elicited lifestyle changes, with 23 themes emerging from 62 codes. As many as 97 participants expressed feelings of isolation, while 63 reported restricted mobility. Finally, participants discussed determinants that influenced their vaccination decisions, in which 46 barriers and four facilitators were identified and mapped onto nine overarching themes. Themes around the CFIR domains Individuals, Inner Setting, and Outer Setting were most prominent concerning vaccine adoption. As for barriers associated with the constructs limited available resources and low motivation, 55 (41%) and 46 (34%) of participants, respectively, mentioned them most frequently. Other frequently mentioned barriers to COVID-19 vaccines fell under the construct policies & laws, e.g., vaccine mandates as a condition of employment. Overall, these findings provide a comprehensive and contextually rich understanding of pandemic experiences and impact, along with determinants that have influenced participants' vaccination decisions. Furthermore, the data revealed diverse experiences within Black communities, including severe infections, stigma, and vaccine-related challenges, highlighting the importance of targeted interventions, support, and consideration of social determinants of health in addressing these effects.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".