Population-level evaluation of ParticipACTION’s 150 Play List: a mass-reach campaign with mass participatory events
Bibliographic record
Abstract
Best practice in mass reach physical activity campaigns includes a role for communities to support the initiative with sustained programs and shorter-term events. The purpose of this paper was to report on an outcome evaluation of ParticipACTION’s 150 Play List, a population-level, year-long, national mass reach campaign that included community events. Participants (N = 1,185) were recruited in the last month of the program to complete a questionnaire measuring demographic information, leisure-time physical activity, campaign awareness, attitudes, intentions, and behavioral trialing. Data were also collected, measured using cell phone proximity, on the number of people who attended 150 Play List events, which ranged from large-scale (e.g. national sports events) to smaller community events. Approximately 43% of respondents were aware of the 150 Play List and 19.5% reported participating in some way (e.g. visiting the website). Almost half of the participants who were aware of the campaign reported increased sport or physical activity-related intentions. Among those who participated, 90.6% reported trying at least one physical activity or sport related behavior as a result of the 150 Play List, whereas only 27.5% (n = 75) of those who were aware but did not participate in the 150 Play List tried a behavior. Event attendance goals were mostly met or exceeded. The 150 Play List was valued by those aware of it and the campaign was related to interest in sport and physical activity in Canada. The community events had potential to augment campaign effects but adequate evaluation requires sufficient resources.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".