Assessing the Safety of Children's Playgrounds from the Parents' Point of View: A Case of the Third District of Tehran
Bibliographic record
Abstract
Undoubtedly, playgrounds should be safe and secure environments for children so that children can play in them, enjoy the game and gain different experiences. Despite the above, the statistics regarding the playground environmental incidents in urban areas indicate a lack of proper attention to the safety of playgrounds. The purpose of this study is to evaluate the safety of children’s playgrounds from the perspective of parents in district 3 of Tehran. The sample of the present study included 295 children from district 3 of Tehran, Iran. The data was collected by the use of standard questionnaires. Also, in order to organize, summarize and describe the data, descriptive statistics (frequency, percentage, mean and standard deviation) were implemented, and in order to test statistical assumptions and prioritization with the normally distributed data, T-Hotelling test and Friedman test were performed. It should be noted that in this study, SPSS software version 23 was used to analyze the data, also the level of reliability of the scale used in this study is 95%. The results of the t-hotelling test showed that the safety of design, the safety of the equipment and the safety of the environmental features in children's playgrounds were in good condition in Tehran's third district. The differences and priorities among these components are also identified
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".