Development and validation of an instrument to evaluate school-health implementation: a sequential mixed-methods approach
Bibliographic record
Abstract
BACKGROUND: Comprehensive School Health (CSH) is an internationally recognized approach that transforms the whole school environment and culture of the school, while wholistically addressing school health. Research has demonstrated the effectiveness of taking a CSH approach to support the creation of health-enhancing behaviors among students while also improving educational outcomes. Despite its effectiveness, there is currently a lack of evaluative tools for schools and school authorities (i.e., school districts, divisions, boards) to measure wholistic shifts in school culture and the conditions present to support implementation. METHODS: Using a sequential mixed-methods approach, we developed and piloted a tool for use by schools and school authorities to plan and evaluate their CSH efforts using the previously established Essential Conditions for taking a CSH Approach. Phase 1 utilized a qualitative, participatory approach to develop the tool and assess face and content validity. Phase 2 utilized a quantitative approach to pilot test and examine the construct validity, and internal consistency of the instrument. RESULTS: Phase 1 results provide evidence of content/face validity for the instrument, with Phase 2 providing promising results indicating high reliability of many of the survey items and mixed results for construct validity, likely due to sample size limitations. At the school level all but one Essential Condition reached an acceptable level of internal consistency (Cronbach's alpha less than 0.70), and six of eight Essential Conditions at the school-authority level met the acceptable threshold value. At the school level, standardized root mean squared residual (SRMR) values for all but one Essential Condition indicated a good fit; at the school-authority level, SRMR values for six of eight Essential Conditions indicated a good fit. As most analyses did not produce chi-square estimates, model fitting results are interpreted with caution. CONCLUSIONS: This study adds to the existing knowledge base for CSH implementation, through the development of a user-friendly evaluative tool for ongoing data collection related to healthy school community process indicators, with encouraging pilot results. This has important practical application and can benefit the broad adoption, scaling, and long-term evaluation of interventions taking a CSH approach.
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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 | 0.162 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".