Correlates of health and healthcare performance: applying the Canadian health indicators framework at the provincial-territorial level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Since, at the health system level, there is little research into the possible interrelationships among the various indicators of health, healthcare performance, non-medical determinants of health, and community and health system characteristics, we conducted this study to explore such interrelationships using the Canadian Health Indicators Framework. METHODS: We conducted univariate correlational analyses with health and healthcare performance as outcomes using recent Canadian data and the ten Canadian provinces and three territories as units of the analyses. For health, 6 indicators were included. Sixteen healthcare performance indicators, 12 non-medical determinants of health and 16 indicators of community and health system characteristics were also included as independent variables for the analysis. A set of decision rules was applied to guide the choice of what was considered actual and preferred performance associations. RESULTS: Health (28%) correlates more frequently with non-medical determinants than healthcare does (12%), in the preferred direction. Better health is only correlated with better healthcare performance in 13% of the cases in the preferred direction. Better health (24%) is also more frequently correlated with community and health system characteristics than healthcare is (13%), in the preferred direction. CONCLUSION: Canadian health performance is a function of multiple factors, the most frequent of which may be the non-medical determinants of health and the community characteristics as against healthcare performance. The contribution of healthcare to health may be limited only to relatively small groups which stand to benefit from effective healthcare, but its overall effect may be diluted in summary measures of population health. Interpreting multidimensional, multi-indicator performance data in their proper context may be more complex than hitherto believed.
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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 | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| 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 it