Demonstrative development of City Health Profile in Healthy City Project
Bibliographic record
Abstract
Objectives: Although many cities have adopted Healthy Cities approach in Republic of Korea, few studies have been reported about city health profile. So we report a case of city health profile made of subjective indexes and objective indicators using available recent evidence. Methods: To assess subjective city health indexes, questionnaire survey was implemented to public officers and citizen adapting the 'Signs of progress, signs of caution of 12 stage tool from Ontario Healthy Community Coalition. Based on recent literature objective city health indicators were collected for time-series comparison and for the comparison with those of larger province mainly using Korean Statistical Information Service. Results: Subjective city health indexes were successfully constructed in four areas including human health, environment, social and economic area. The score was especially low in environmental area. Specific items in each area for improvement were identified. Objective city health indicators were collected for three year time-series comparison and for the compared with those of larger province. Conclusions: City health profile comprised of subjective city health indexes and objective city health indicators could successfully be made from primary survey and secondary data in a medium-sized Korean city. That City health profile was useful in subsequent city health planning through participatory process.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".