Population health indicators across Ontario’s Public Health Units: a cross-sectional analysis of the Canadian Community Health Survey
Bibliographic record
Abstract
Background Currently, 34 public health units (PHUs) in Ontario deliver public health programs and services to reduce preventable diseases, promote and protect health of their communities, and reduce persistent health inequities. Changes to the structure of Ontario PHUs have been proposed. This analysis compares the current 34 Ontario PHUs based on key health indicators for the purpose of determining local health needs in delivering public health programs and as a baseline for measuring the effect of any future changes to PHU structure. Methods We used data from the 2015–2016 Canadian Community Health Survey (CCHS), a voluntary cross-sectional survey about health status of Canadians. Twenty-one health indicators measured by the CCHS and particularly relevant to PHU responsibilities were identified and compared across units. In this descriptive, cross-sectional analyses we used survey-weighted frequency calculations of the selected indicator variables by PHU and χ2 analyses to test differences in indicator distribution across PHU. Results All indicators except for sex were distributed unevenly by PHU. We particularly highlight differences across units in modifiable indicators and risk factors such as obesity, fruit and vegetable consumption, physical inactivity, smoking, and access to primary care physicians. Impact of the study While all PHUs strive towards the same mandated responsibilities, considerable variations in health indicators exist between health units. This underscores the necessity for PHUs to tailor programs and deliver services based on local needs. Future changes to PHU structure must be tested against baseline to determine if they ameliorate or exacerbate health inequities in 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".