Barriers and facilitators to the implementation of a school-based physical activity policy in Canada: application of the theoretical domains framework
Bibliographic record
Abstract
BACKGROUND: In British Columbia Canada, a Daily Physical Activity (DPA) policy was mandated that requires elementary school teachers to provide students with opportunities to achieve 30 min of physical activity during the school day. However, the implementation of school-based physical activity policies is influenced by many factors. A theoretical examination of the factors that impede and enhance teachers' implementation of physical activity policies is necessary in order to develop strategies to improve policy practice and achieve desired outcomes. This study used the Theoretical Domains Framework (TDF) to understand teachers' barriers and facilitators to the implementation of the DPA policy in one school district. Additionally, barriers and facilitators were examined and compared according to how the teacher implemented the DPA policy during the instructional school day. METHODS: Interviews were conducted with thirteen teachers and transcribed verbatim. One researcher performed barrier and facilitator extraction, with double extraction occurring across a third of the interview transcripts by a second researcher. A deductive and inductive analytical approach in a two-stage process was employed whereby barriers and facilitators were deductively coded using TDF domains (content analysis) and analyzed for sub-themes within each domain. Two researchers performed coding. RESULTS: A total of 832 items were extracted from the interview transcripts. Some items were coded into multiple TDF domains, resulting in a total of 1422 observations. The most commonly coded TDF domains accounting for 75% of the total were Environmental context and resources (ECR; n = 250), Beliefs about consequences (n = 225), Social influences (n = 193), Knowledge (n = 100), and Intentions (n = 88). Teachers who implemented DPA during instructional time differed from those who relied on non-instructional time in relation to Goals, Behavioural regulation, Social/professional role and identity, Beliefs about Consequences. Forty-one qualitative sub-themes were identified across the fourteen domains and exemplary quotes were highlighted. CONCLUSIONS: Teachers identified barriers and facilitators relating to all TDF domains, with ECR, Beliefs about consequences, Social influences, Knowledge and Intentions being the most often discussed influencers of DPA policy implementation. Use of the TDF to understand the implementation factors can assist with the systematic development of future interventions to improve implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".