The mechanisms by which a whole-school intervention might improve sexual health: qualitative realist research nested in a trial in English secondary schools
Bibliographic record
Abstract
Whole-school interventions go beyond classroom education, promoting health by modifying school environments. These can be effective in delaying sexual debut and increasing contraception use but mechanisms are poorly understood. Qualitative research within realist evaluation can explore mechanisms via building 'context-mechanism-outcome configurations', describing how interventions trigger mechanisms that interact with context to generate outcomes. We explored these for the Positive Choices whole-school sexual health intervention within the intervention arm of a randomised trial conducted 2021-2025. Using 'dimensional analysis', we analysed 52 interviews with teachers and 40 focus-groups involving 266 students from 22 English secondary schools. Our results suggest seven mechanisms through which whole-school interventions might 'work': improving knowledge using diverse pedagogies; improving confidence and ability to talk by normalising talk about sexual health; changing gender attitudes through challenging stereotypes and providing insights and empathy with others' perspectives; promoting access to sexual health and other services via helping students understand their needs and entitlements; building school engagement by providing new student roles on decision-making groups; increasing inclusion of sexual-minority students by normalising consideration of non-heterosexual identities and practices; and reducing sexual harassment and abuse by helping students understand consent and when to intervene in harassment. Contextual contingencies included: high initial student needs; teacher skills and commitment; and school commitment and capacity. Our research suggests novel mechanisms via which whole-school interventions might promote sexual health. Quantitative analyses will now be conducted to examine these mechanisms and contingencies.
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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 | 0.068 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".