Using discussion to inform action: Formative research on nature-based physical activity as a means of fostering relatedness for girls in physical and health education
Bibliographic record
Abstract
The long-standing challenges and issues associated with girls’ disengagement from secondary school physical and health education (PHE) are serious and well documented. This disengagement has provided the incentive for the examination of alternative strategies to facilitate girls’ engagement in PHE. This paper discusses the first phase in a formative research process designed to develop a resource manual to help teachers utilize nature-based physical activity (NBPA) as a means of fostering relatedness for girls in PHE. Participating teachers collaborated and generated specific NBPA ideas and pedagogical strategies during an all-day planning session. Four focus groups with the teachers ( N = 20) were used to identify ways to develop NBPA interventions. Five broad topics are reported: (a) defining NBPAs, (b) specific NBPAs to use in PHE, (b) how NBPA can foster relatedness, (d) how NBPA in PHE differs from outdoor education, and (e) barriers to implementing NBPA in PHE. This paper emphasizes the valuable contribution of formative research to the integrity and fidelity of an intervention as well as to quality practice in the implementation of theory-based PHE initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".