Advancing Healthcare for COVID-19 by Strengthening Providers’ Capacity for Best Practices in African, Caribbean and Black Community Service Provision in Ontario: A Multisite Mixed-Method Study Protocol
Bibliographic record
Abstract
BACKGROUND: The ongoing COVID-19 pandemic has emerged as an unprecedented challenge for public and private life, and healthcare systems worldwide. African, Caribbean, and Black communities (ACB) represent some of the most vulnerable populations in terms of their susceptibility to health hazards, difficulty receiving adequate health care and relatively lower chances of recovery. OBJECTIVES: The main aim of this study is to improve the health system’s response during and after the COVID-19 pandemic by developing evidence-based models to inform policy and collaborative best practices to mitigate its spread and ameliorate related health consequences in vulnerable communities. METHODS: This is a mixed-method, multisite study based in Ottawa and Toronto that will involve in-depth qualitative interviews and surveys using a structured questionnaire. Data will be analyzed using NVivo for qualitative interviews, Stata 16 and IBM SPSS version 26 for statistical analyses. DISCUSSION: The findings of this study gained from highly professional health practitioners will produce strong evidence on current gaps in knowledge and practice in the healthcare system’s capacity to meet the health needs of ACB population. The distinct insights and perspectives will be disseminated with policymakers and researchers at all levels which will facilitate strategic policy making with the goal of addressing the unique challenges for health
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".