Developing a culturally relevant and feasible data collection protocol to measure physical activity, sleep, and sedentary time in high school students in Guadalajara, Mexico
Bibliographic record
Abstract
Background: The benefits of achieving movement behaviour recommendations (i.e., an adequate balance of physical activity, sleep, and sedentary time over a 24-hour period) in young adulthood are well-established. However, in Mexico, nationally representative self-reported data suggest that only 5% of adolescents (15-19 years) meet these recommendations. Promoting movement behaviours among Mexican adolescents is a public health imperative. To develop and assess effective interventions, it is essential to identify data collection protocols that are feasible and culturally relevant in a Mexican setting. Methods: From October 2022-May 2023, the research team met with stakeholders (school board officials, school principals, parents, and high school students) in Guadalajara, Mexico to introduce and discuss a project to measure movement behaviours in high school students using Fitbits (a low-cost alternative to research-grade accelerometers) and a questionnaire. Local university students were trained as research assistants (RAs) and helped test Fitbits and pilot the configuration process. The questionnaire was developed and piloted with the ultimate intended population. Results: Meetings with stakeholders determined culturally relevant procedures for selection, recruitment, consent, and data collection. Eleven university students received RA training. Piloting highlighted essential space, time, technology and human resource needs for a future study. Eighteen high school students (33% women, average age 15.3) completed the questionnaire, taking 7-19 minutes. Students’ lack of access to wi-fi was a barrier to data collection. Conclusion: The data collection protocol is feasible and culturally relevant. It will be used to assess the movement behaviour of 600 high school students from August-November 2023.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".