Design and psychometric evaluation of health system intervention assessment tools for children in floods
Bibliographic record
Abstract
BACKGROUND: Flood is one of the most frequent disasters in Iran, which has highly affected the population and consequences on the health system. Children as the most vulnerable group too need to receive health services during floods. The aim of the present study was to develop a national tool for evaluating the provision of health services to children in floods. MATERIAL AND METHODS: This study is a sequential-exploratory mixed method study that consists of two qualitative and quantitative stages. The qualitative part includes the analysis of documents and panel of experts while the quantitative part includes the design and validation of the tools. RESULTS: In this study, organizations providing health services to children were first identified, and according to their mission the relevant items were extracted and the initial checklist was designed. Then validity and reliability of the tools were done. The content validity ratio and content validity index for the tool were 59 and 98%, respectively. Cronbach's alpha and intraclass correlation coefficient were determined as 0.7 and 0.964, respectively. The final tool was presented with 64 items. CONCLUSIONS: The response program, the scope of interventions, service coverage, and the effectiveness of the response after the flood can help reduce the risk of disasters in children. Using the assessment tool of evaluating the health services to children can assist the stakeholder organizations to meet the standards and best quality of services. Assessing the needs of the children affected by floods, identifying the strengths and weaknesses of health services, and proposing corrective strategies according to the information extracted from this tool are other achievements of this study.
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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 | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".