Position of Social Determinants of Health in Urban Man-Made Lakes Plans
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: A social determinants approach proposes that enhancing living conditions in areas such as income, housing, transportation, employment, education, social support, and health services is central to improving the health of urban populations. Urban development projects can be costly but have health impacts. The benefit derived from the creation of man-made lakes in developing countries is usually associated with great risks; however, the evidence for physical and non-physical health benefits of urban man-made lake is unclear. The aim of this paper is to formulate a conceptual framework of associations between urban man-made lakes and social determinants of health. METHOD: This study was a qualitative study carried out using one focus group discussion and 16 individual interviews. Data were analyzed based on deductive-inductive content analysis approach. RESULTS: Participants' points of view were analyzed within 261 codes. Data analysis matrix was the conceptual framework of social determinants of health commission and its sub-groups, thus, two structural and mediating determinants categories as well as their sub-sets were created accordingly. In addition, some extra sub-sets including environment, air quality, weather changes, noise pollution, pathogenesis, quality of life, shortage of available resources, region popularity, ethnicity, tourism, social and physical development of children, unintentional injuries, aesthetic, and spirituality were extracted beyond the matrix factors, which were placed in each of above categories based on their thematic content. CONCLUSION: This paper has illustrated that the quality and type of man-made lake provided within communities can have a significant and sustained impact on community's health and wellbeing. Therefore, in order to strengthen positive effects and reduce negative effects of any developmental projects within community, their impacts on public health should be taken into consideration.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".