Ranking the Effects of Urban Development Projects on Social Determinants of Health: Health Impact Assessment
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Health impact assessment (HIA) offer a very logical and interesting approach for those aiming to integrate health issues into planning processes. With a lot of works and plans waiting to be done (e.g., developing and updating plans, counseling planning commissions, cooperation with other organizations), planners find it difficult to prioritize health among a variety of possible issues and solutions they confront. METHODS: In the present article, first, the list of social determinants of health associated with Chitgar man-made lake was extracted out using a qualitative method and with content analysis approach, and then they were prioritized using analytic hierarchy process. RESULTS: 28 social determinants of health including "intermediary" and "structural" determinants were extracted out. Regarding positive effects of lake on these determinants, "recreational services" and "traffic" received the highest and the lowest weights with 0.895 and 0.638 respectively among structural determinants and with consideration to "construction" option. Furthermore, among intermediary determinants for "construction" option, sub-criteria of both "physical activity" and "air quality" received the final highest weight (0.889) and "pathogenesis" indicated the lowest weight with 0.617. Moreover, lake demonstrated the highest negative effects on "housing" among "structural" determinants which it takes the highest weight (0.476) in "non-construction" option. Additionally, lake had the highest negative effects on "noise pollution" among "intermediary determinants" and it takes the highest weight (0.467) in "non-construction" option. CONCLUSION: It has been shown that urban development projects such as green spaces, man-made lakes … have a huge range of effects on community's health, and having not considered these effects by urban planners and mangers is going to confront urban health with many challenges.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".