Effectiveness of School Based Recruitment Procedures and Modular Data Collections
Bibliographic record
Abstract
Purpose: The School Health Action, Planning and Evaluation System (SHAPES) is a school-based data collection and knowledge exchange system designed to improve the health of youth. This paper outlines the design of the SHAPES study, examines the impact of different school recruitment models on participation rates, and examines the impact of using two different research modules during data collection on the prevalence of core behaviours being measured. Methods: In total, 76 schools were recruited from seven health regions and data were collected using the SHAPES Tobacco (TM) and Physical Activity Modules (PAM). Results: It was found that school recruitment rates were higher when both the researchers and the health unit, worked together to recruit schools. Significant differences were found between students who completed the TM and students who completed the PAM with respect to body mass index, smoking susceptibility, the number of friends who smoke, and the number of active friends. Conclusions: This paper provides valuable real-world insight for future researchers interested in performing population-level school-based studies of youth risk behaviours. Our experience suggests that a modular approach to data collection is feasible and that recruitment rates are improved when researchers work in collaboration with health unit staff who have existing relationships with schools.
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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 | 0.364 | 0.488 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".