Vaccines for all: A formative evaluation of a multistakeholder community-engaged COVID-19 vaccine outreach clinic for migrant communities
Bibliographic record
Abstract
Background: Racialized, low-income, and migrant populations experience persistent barriers to vaccines against COVID-19. These communities in East and Northeast Calgary were disproportionately impacted by COVID-19, yet faced vaccine access barriers. Diverse multi-stakeholder coalitions and community partnerships can improve vaccine outreach strategies, but how stakeholders perceive these models is unknown. Methods: We conducted a formative evaluation of a low-barrier, community-engaged vaccine outreach clinic in Calgary, Alberta, Canada, on June 5-6, 2021. We delivered an online post-clinic survey to clinic stakeholders, to assess whether the clinic achieved its collectively derived pre-specified goals (effective, efficient, patient-centered, and safe), to asses whether the clinic model was scalable, and to solicit improvement recommendations. Survey responses were analyzed using descriptive statistics and thematic analysis. Results: Overall, 166/195 (85%) stakeholders responded. The majority were from non-healthcare positions (59%), between 30 and 49 years of age (87/136; 64%), and self-identified as racialized individuals (96/136; 71%). Respondents felt the clinic was effective (99.2%), efficient (96.9%), patient-centered (92.3%), and safe (90.8%), and that the outreach model was scalable 94.6% (123/130). There were no differences across stakeholder categories. The open-ended survey responses supported the scale responses. Improvement suggestions describe increased time for clinic planning and promotion, more multilingual staff, and further efforts to reduce accessibility barriers, such as priority check-in for people with disabilities. Conclusion: Diverse stakeholders almost universally felt that this community-engaged COVID-19 vaccine outreach clinic achieved its goals and was scalable. These findings support the value of community-engaged outreach to improve vaccine equity among other marginalized newcomer communities.
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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 | 0.061 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".