Identification of Health Equity Measures for a Canadian Regional, Academic Hospital: A Rapid Review of Literature
Bibliographic record
Abstract
This study is situated in Northeastern Ontario, which like other regions in Canada, requires a health equity strategy that extends to a hospital’s role in the collection and use of locally relevant health equity data. The Canadian health care system aims to provide publicly funded ‘universally accessible’ health care, but struggles with these objectives. The study is motivated by Northeastern Ontario’s longstanding health inequities and stems from comprehensive efforts to answer a question presented by regional, academic health sciences centre, Health Sciences North’s (HSN), Board of Directors: How can we measure health equity? This rapid review of literature is part of a larger, three-armed study titled “Identification of Health Equity Data Indicators for a Northeastern Ontario Regional Academic Health Sciences Centre: A Multi-Methods Study.” It seeks to understand what and how health equity data is identified, collected, analyzed and managed in the context of Canadian hospitals. A rapid review methodology was chosen for the rapid translation of knowledge to inform a subsequent environmental scan and community and partner engagement sessions as part of the larger study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.106 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.054 | 0.051 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".