Results of a Global Survey of Experts to Categorize the Suitability of Interventions for Inclusion in School Health Services
Bibliographic record
Abstract
PURPOSE: This global survey of experts assessed the suitability of different health-related interventions for inclusion in school health services (SHSs) to inform development of the World Health Organization global guideline on SHSs. METHODS: A review of 138 global World Health Organization publications identified 406 health service interventions for 5- to 19-year-old individuals. These were consolidated, pretested, and pilot-tested in a questionnaire as 86 promotion, prevention, care, or treatment interventions. A total of 1,293 experts were identified through purposive sampling of journal databases and professional networks. In July 2019, experts were invited to complete the questionnaire online in Arabic, Chinese, English, French, Russian, or Spanish. Respondents categorized each intervention as essential, highly suitable, suitable, or unsuitable in SHSs (everywhere or in certain geographic areas only). They could also suggest interventions. RESULTS: Interventions categorized most often as "Essential in SHSs everywhere" (70%-80%) are related to health promotion and health education. Clinical interventions categorized most often in this way (60%-68%) are related to immunization, screening, assessment, and general care. Interventions categorized most often as "Essential in SHSs in certain geographic areas only" (27%-49%) are related to immunization, mass drug administration, and health promotion. Interventions categorized most often as "Unsuitable in SHSs anywhere" (12%-14%) are related to screening of noncommunicable conditions. There were no important regional differences. Of 439 respondents from 81 countries, 188 suggested 378 additional interventions. Question order effect and/or purposive sampling biases may have influenced both quantitative and qualitative results for different types of intervention. CONCLUSIONS: Favorable responses to almost all interventions supported their World Health Organization guideline inclusion but provided little guidance for intervention prioritization.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".