Community health assessment through an income-related health equity lens: a retrospective case study of three regional health authorities in Manitoba
Bibliographic record
Abstract
Community Health Assessment (CHA) is a legislated process in Manitoba, Canada, which provides an overview of health in each Regional Health Authority (RHA). This process is important for operational and strategic planning, and provides an opportunity to explore health inequities. The CHA process in Manitoba has not been reviewed from an income-related health equity (IRHE) lens to date. The purpose of this dissertation was to learn how Manitoba’s RHAs incorporated an IRHE lens in their CHA process and to identify the facilitating and impeding factors for incorporating such a lens. This retrospective research project used case study methodology. Three cases were selected due to their geographic and demographic diversity. Data collection involved document reviews and individual interviews with CHA staff and board members/senior management using a semi-structured interview guide. Interviews were audio-recorded and transcribed verbatim. Categorical aggregation was used to establish themes from the data guided by the IRHE Framework for CHA (adapted from the National Collaborating Centre for Determinants of Health, 2011). The results of this research show that the RHAs did not apply an explicit IRHE lens to the third cycle CHA process, although it was recognized by almost all participants as important work. Several important similarities arose across the RHAs, further validating the findings of this study. Barriers to IRHE-focused CHA work included factors such as other competing priorities, lack of a provincial IRHE-focused framework, confusion over true CHA partnerships, capacity issues, lack of action due to the CHA process largely owned by health departments including Manitoba Health and the RHAs, and a lack of awareness of the social determinants of health (SDOH). Some of the facilitating factors included provincial structures such as the CHA Network (CHAN) and the CHA guidelines and a general consensus that CHA is a valued and respected process. Several recommendations are made, including suggestions for an improved CHA process focused on IRHE or health equity in general. Future research in health studies and/or evaluations should consider using a health equity lens.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".