The Maiden Voyage: Exploring the Multisectoral Partnership Process of Creating a Physical Literacy Enriched Community
Bibliographic record
Abstract
BACKGROUND: Understanding children interact with, and in, a wide range of contexts (home, school, and community) on a daily basis, interventions that are designed to address a combination of these contexts are critical to the development of physical literacy. To our knowledge, this is one of the first multicontextual and multisectoral physical literacy interventions delivered where the effects were measured and reported. METHODS: Given the uniqueness of this intervention, we sought to answer the question "What were the experiences of the individuals representing the multisectoral partnership involved in the process of creating a multicontextual physical literacy enriched community intervention?" Thematic analysis was used to analyze data collected from interviews, document analyses, and participant observation. RESULTS: The results identified key components, presented as 4 themes, to be considered in developing a successful partnership approach to creating a physical literacy enriched community, including alignment of strategies when working in a community, clear goals and expectations, strong communication and leadership, and transparency regarding capacity and commitment. CONCLUSIONS: As this was one of the first multicontextual and multisectoral physical literacy interventions, it was important to document the experiences of creating the program to encourage future growth in physical literacy and multicontextual intervention strategies, as well as develop suggested best practices. By gaining a better understanding of strategies that did and did not work in this multisectoral partnership, we can begin to compile successful approaches for future efforts to create a physical literacy enriched community.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| 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".