Advancing health equity in Nova Scotia by exploring gaps in healthcare delivery: a mixed methods protocol
Bibliographic record
Abstract
Population health issues are addressed by various regional initiatives in the Canadian province of Nova Scotia (NS). A need for research on the root causes of health inequities suggests there may be a lack of evidence to inform current initiatives within the region. To address this gap, a three-phase sequential mixed methods study called Advancing Health Equity in NS by Exploring Gaps in Healthcare Delivery will operationalize Intersectionality Theory and employ an integrated knowledge translation approach to identify and explore gaps in health service delivery. This will promote a better understanding of how to improve the integration of health equity in health service and delivery systems and thus population health and well-being. The following objectives will be addressed in each phase: 1) create an inventory of NS-relevant knowledge that relates to health equity, 2) examine the integration of health equity in NS health service and delivery systems using a context-specific health equity lens, and 3) mobilize knowledge on how gaps in service delivery can be addressed to improve the integration of health equity and better meet the needs of people living in NS. The study results from this protocol will be used to integrate health equity in NS health service and delivery systems, enhancing the quality of care for populations rendered vulnerable by structural inequalities, and working to prevent negative impacts to health and wellbeing.
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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 | 0.109 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".