Community-led initiatives bridging the gap to provide linguistically and culturally tailored health and social services in Parc-Extension, Montréal
Bibliographic record
Abstract
Background: Parc-Extension (PE) has the greatest immigrant population in Montréal. PE had a lower proportion of vaccinations compared to the rest of Montreal (19.9% vs 30.8%), during Québec’s first rollout of COVID-19 vaccines. By August 2021 PE’s proportion of first dose vaccinations surpassed Montreal’s (77.9% vs 74%). This is attributable to building vaccine acceptance through community-led strategies that addressed social determinants of health (SDOH) impacting immigrant and asylum seeker communities. Objective: To examine the perspectives of PE residents around how SDOH influence their access to health and social services, including COVID-19 vaccines. Methods: We conducted semi-structured interviews with PE residents to explore which SDOH contributed to the accessibility to health and social services during all waves of the pandemic to date. The interviews were recorded and transcribed using Otter.ai and HappyScribe for English and French transcripts, respectively. We performed thematic content analysis. Coding was done by three co-authors and discrepancies were resolved during analysis meetings with all study authors. Results: We conducted 47 interviews (French: 27, English: 17, Urdu: 3) between June and October 2022. Three themes were identified: inadequate governmental support for immigrants and asylum seekers exacerbate SDOH; language barriers influence care; and inaccessibility to healthcare providers creates a “clinical desert”. Discussion: Due to inadequate support at the governmental level, there is an unjust burden placed on PE community organizations to ensure that PE residents are receiving appropriate care. Improving access to services amongst immigrant groups requires consideration of SDOH and fostering trusting partnerships between governments and community organizations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".