Health equity and rurality in Northern Ontario
Bibliographic record
Abstract
Background: The current climate in Ontario, Canada is one where access to health and social services, healthcare experience and health/social outcomes vary widely across the province. The existing health disparities in Ontario disproportionately affect those living in rural and northern areas. Current indicators used to measure this variability have been developed in the context of health systems in more densely populated areas and may not be relevant for more rural and remote geographic areas. As such, the objectives of this thesis were: (1) to develop a health equity measurement approach specific to Northern Ontario based on input from Northern Ontario health decision-makers, and (2) to operationalize a rurality measurement approach for Northern Ontario. Methods: This two-phase exploratory sequential mixed methods study included a qualitative inquiry followed by a descriptive rurality measurement. The first phase explored health equity measurement in the context of Northern Ontario through in-depth interviews with Northern Ontario health equity key informants. The resulting thematic analysis informed a proposed Northern Ontario health equity measurement approach and the rurality stratifier exploration in phase-two. The second phase included a descriptive analysis using secondary data. The two rurality measurement approaches included were Statistical Area Classification Type and the Remoteness Index. Chi -squared tests for independence were used to assess the level of association between all classification methods including alternate categorization approaches within the Remoteness Index measure. Results: The thematic analysis in phase-one revealed four health equity indicators of relevance to Northern Ontario: infant mortality, overall mortality, perceived health status, and satisfaction of health care received. Furthermore, two stratifiers were identified as uniquely important to measuring health equity in Northern Ontario contexts. These two stratifiers included geographic position (rurality), as well as material welfare (income). The descriptive analysis of the rurality stratifier in phase-two recommended two methods of categorization using the Remoteness Index to consider as a complement or replacement to the Statistical Area Classification Type approach. Conclusion: This exploration of health equity measurement in the context of Northern Ontario proved to be a feasible and productive way to engage key informants in health equity indicator/stratifier selection and recommendation. Certain health equity stratifiers – including rurality – are elusive to define and measure; however, the Statistical Area Classification Type and Remoteness Index should both be considered as rurality measures in Northern Ontario.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".