Health Impact Assessment of Urban Development Project
Bibliographic record
Abstract
BACKGROUND: Health impact assessment (HIA) has emerged to identify those activities and policies likely to have major impacts on the health of a population. METHOD: In this research, qualitative method was applied to identifying health determinants that urban man made lake affect on them, formatting and weighing the hierarchy of the factors, calculating AHP, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for decide and ranking alternatives. RESULTS: According to the results of the study, from the structural determinants point of view, the most positive effect of man-made lake was on Recreational services by 89.5% and the most negative one was on housing. According to intermediary determinants and general average, the most positive effect of lake was on physical activity and quality of air by 88.9% and the most negative one was on noise pollution by 46.7%. Ultimately, considering the positive and negative effects of lake between constructing and not constructing the lake option, the construction option was selected. CONCLUSION: There is substantial potential to improve public health by bringing decision makers' attention to the health consequences of their actions; city councilpersons, zoning commissioners, and other decision makers typically have little background in health.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".