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 \nservices, healthcare experience and health/social outcomes vary widely across the province. The \nexisting health disparities in Ontario disproportionately affect those living in rural and northern \nareas. Current indicators used to measure this variability have been developed in the context of \nhealth systems in more densely populated areas and may not be relevant for more rural and \nremote geographic areas. As such, the objectives of this thesis were: (1) to develop a health \nequity measurement approach specific to Northern Ontario based on input from Northern Ontario \nhealth decision-makers, and (2) to operationalize a rurality measurement approach for Northern \nOntario. \nMethods: This two-phase exploratory sequential mixed methods study included a qualitative \ninquiry followed by a descriptive rurality measurement. The first phase explored health equity \nmeasurement in the context of Northern Ontario through in-depth interviews with Northern \nOntario health equity key informants. The resulting thematic analysis informed a proposed \nNorthern Ontario health equity measurement approach and the rurality stratifier exploration in \nphase-two. The second phase included a descriptive analysis using secondary data. The two \nrurality measurement approaches included were Statistical Area Classification Type and the \nRemoteness Index. Chi -squared tests for independence were used to assess the level of \nassociation between all classification methods including alternate categorization approaches \nwithin the Remoteness Index measure. \nResults: The thematic analysis in phase-one revealed four health equity indicators of relevance to \nNorthern Ontario: infant mortality, overall mortality, perceived health status, and satisfaction of \nhealth care received. Furthermore, two stratifiers were identified as uniquely important to \nmeasuring health equity in Northern Ontario contexts. These two stratifiers included geographic \nposition (rurality), as well as material welfare (income). The descriptive analysis of the rurality \nstratifier in phase-two recommended two methods of categorization using the Remoteness Index \nto consider as a complement or replacement to the Statistical Area Classification Type approach. \nConclusion: This exploration of health equity measurement in the context of Northern Ontario \nproved to be a feasible and productive way to engage key informants in health equity \nindicator/stratifier selection and recommendation. Certain health equity stratifiers – including \nrurality – are elusive to define and measure; however, the Statistical Area Classification Type \nand Remoteness Index should both be considered as rurality measures in Northern Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".