Make Room for Play: An Evaluation of a Campaign Promoting Active Play
Bibliographic record
Abstract
In the context of rising screen time, only a third of Canadian children are achieving adequate amounts of active play, an important source of physical activity. ParticipACTION, a national not-for-profit organization, created the "Make Room for Play" campaign targeting parents with television advertisements depicting how screen time takes away from active play. The advertisements featured children engaging in active play (e.g., jump rope) while a black screen progressively sequesters the room for them to play. This study's purpose was to evaluate the campaign using the hierarchy of effects model, a framework for conceptualizing the impact of mass media campaigns. It was hypothesized that recall would relate to intermediate (e.g., cognitions, self-efficacy) and distal (e.g., parental support) factors. Twenty-six percent of the general population and caregiver samples surveyed (N = 1576) recalled (unaided) the advertisement and 45.9% recalled when prompted. Parental support was significantly higher in those recalling the campaign, p = .009. Twenty-four percent of parents reporting unaided recall (versus 14.0% of those not) tried to engage in active play with their children and 21.2% (versus 12.0%) tried to create opportunities for children to engage in play. Strengths and limitations of mass media approaches targeting active play and screen time are discussed.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".