A Physical Activity Practice Index for Older Students and Adults
Bibliographic record
Abstract
This article puts forward a physical activity practice (PAP) index that could be used by older students (high school and college level) and adults who wish to monitor their PAP in order to regulate it. Authors explain the origin of the PAP index, identify PAP aspects such as frequency, duration, intensity and diversity as they relate to the index and describe the simple mechanics of its computation. Then, they discuss physical literacy (PL) awareness in terms of competence, knowledge and understanding, motivation toward PAP, and actual physical activity (PA) engagement. Regulation scenarios are presented in association with individuals’ PAP awareness. Finally, a few suggestions are added with regard to tailored PAP monitoring for more engaged physical literate persons. The PAP index described is intended to help individuals monitor their PAP over time, taking simultaneously into account its intensity and volume whatever the selected activity(ies). Willingness to monitor one’s PAP implies at least a minimal level of PL awareness. Besides reflecting on their PA-related knowledge and PA competency and capacities, individuals ought to understand their reasons for engaging into the regular practice of PA. Based on their answers to such questions, they can then engage into regulation scenarios with the help of monitoring instruments such as the one discussed in this article. Keywords: physical literacy, physical-literacy awareness, physical-activity-practice awareness, FITT formula, PAP monitoring
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".