Improving the Measurement of Health System Performance across the Rural-Urban Continuum
Bibliographic record
Abstract
IntroductionWe report on key performance indicators to highlight quality and variation in health care. Given Ontario’s diverse geography, we have prioritized improving measurement across the rural-urban continuum. This will improve our ability to discern the impact of geography on health care and health status to inform planning and decision making. Objectives and ApproachBuilding on previous work to advance measurement of equity in health care, we struck a technical working group of experts to review methods for stratifying health system performance data by geographic location in the Ontario context. These methods were applied to a set of key performance indicators. The working group’s review of the results of this analysis will lead to recommendations for the best method to refine and standardize how geographic location is measured and stratified. This will improve our ability to discern the impact of geography on health system performance and health status for our suite of public-reporting products. ResultsThe technical working group identified three methodologies for consideration that used linked postal code data: Population Centre (POPCTR), Statistical Area Classification (SAC) and a hybrid POPCTR/SAC methodology. These methods were tested against a set of key performance indicators across dimensions of quality including timeliness, effectiveness, population health and health outcomes. The results show that, in the health system performance dimensions of effectiveness and timeliness, as well as for a subset of health outcomes, there is variation in performance across the urban-rural continuum, though not always in a linear way. This may reflect differences in health care access, health risk factors, sociodemographic or socioeconomic characteristics across the urban-rural continuum. More definitive conclusions and recommendations will be available when the working group meets to review the results. Conclusion/ImplicationsIdentifying a robust methodology for measuring performance across geographic locations will improve our ability to discern the impact of geography on health care including where geography may impact access and effectiveness of services as well as health outcomes. This information will enable better health system planning and decision-making.
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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 | 0.026 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".