PHYSICAL LITERACY: A MODEL TO ENGAGE AND SUPPORT OLDER ADULTS IN PHYSICAL ACTIVITY AND SPORT
Bibliographic record
Abstract
Physical literacy (PL) is a promising strategy to increase physical activity and sport participation across the lifespan. This presentation outlines the developmental process of creating a PL model for older adults, by an expert team of multi-disciplinary academics, non-profit organizations and user groups. The process began with an iterative consensus development process which identified the use of the adopted International Physical Literacy Association within an ecological model approach, reflecting a full range of key characteristics proposed to influence physical literacy in older adults. The model is anchored with the individual (intrapersonal factors) and depicted to have influences from inter-personal, organizational, community, and policy factors. Broader consensus for the PL model was reached using an online Delphi survey. An international group of multi-disciplinary and multi-sectoral Delphi participants who encapsulate the scope of the proposed physical literacy model were invited to participate. Twenty-nine Delphi invitees participated in the first round of the survey with significant consensus being reached for each of the elements of our model (i.e., % responding agree, somewhat agree, or strongly agree). Open-ended feedback from Round 1 was discussed by the expert team and a modified model was distributed in Round 2 of the survey. Twenty-three out of the original 29 respondents completed the second round (79%) and a significant consensus was again achieved. Next steps include determining methods to assess physical literacy in older adults and dissemination of the model.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".