Child health-friendly neighbourhood: a qualitative study to explore the perspectives and experiences of experts and mothers of children under 6 years of age in Tehran, Iran
Bibliographic record
Abstract
OBJECTIVES: Creating health-supportive environments is one of the key strategies for health promotion. The WHO launched the Healthy Cities Initiative which has inspired other international organisations to develop settings-based health initiatives, such as the Child Friendly Cities by UNICEF. Our study aimed to explore the perspectives and experiences of experts, city council staff and mothers of children under 6 years of age in the city of Tehran, Iran regarding child health-friendly neighbourhoods for children of this age group. DESIGN: The purpose of this qualitative research was to investigate the viewpoints and experiences of mothers of children under 6 years old as well as professionals. Data were collected from January to July 2022 through semistructured, indepth interviews using an interview guide. Data were analysed using the directed content analysis method with MAXQDA V.2020 software. SETTING: The study was conducted in Tehran, Iran. PARTICIPANTS: Participants were selected from three main groups: experts, mothers and city council staff. Participants were invited to take part using variation purposive sampling techniques. RESULTS: Data analysis led to a definition of the concept of child health-friendly neighbourhoods for children under 6 years old, with 6 dimensions, 21 subdimensions and 80 characteristics. The six dimensions included the provision of neighbourhood green space, cultural centres, health centres, access to services, transport and security. The characteristics we identified had similarities and differences with UNICEF's Child Friendly Cities. CONCLUSION: The concept of a child health-friendly neighbourhood for children under 6 years old is the result of a health-centred approach to a child-friendly city that provides a deeper understanding of the needs and services required to start a healthy life. This could contribute to further dialogue, research and actions to make all neighbourhoods a health-supportive environment as recommended by the Ottawa Charter for Health Promotion.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".