The short-term effects of a mass reach physical activity campaign: an evaluation using hierarchy of effects model and intention profiles
Bibliographic record
Abstract
BACKGROUND: Mass reach physical activity campaigns are designed to deliver physical-activity related messages to a large population across different media including print, television, radio, and websites. Few evaluations have examined the short-term effects of a mass reach campaign on participants who were engaged with the campaign. The current research examined the short-term effects of the ParticipACTION 150 Play List, a mass reach physical activity campaign, on participants who registered with the campaign website. METHODS: Participants (N = 7801) completed a registration questionnaire measuring demographic information, awareness and recall of physical activity and sport advertising, and self-reported number of activities tried or planned to try from the 150 Play List. A follow-up survey was completed by 1298 participants from the original sample. Additional questions assessed experience with the 150 Play List and attitudes towards campaign advertisements. RESULTS: Approximately 14.5% of participants cited the ParticipACTION 150 Play List and 23.6% mentioned a 'getting active' message when recalling advertisements. Those who named the 150 Play List or getting active reported more activities tried and more activities planned than those who did not. They were also more likely to say they had tried a new activity and planned ongoing participation. It was also found that participants with a disability were more likely to have tried a new activity compared to those not in a minority group. Other correlates of trying new activities at follow-up were younger age, more positive reported experience with the 150 Play List, and more favourable attitudes towards campaign advertisements. Those who did not intend continued participation, or who were unsure at baseline and then decided against continued participation at follow-up, reported they were less sedentary or encouraging others to be active. CONCLUSIONS: This research addresses the gap in evidence regarding the efficacy of mass reach physical activity campaigns by informing whether a year-long campaign like the 150 Play List can be effective in influencing the behavior of those engaged with the campaign. The results reinforce the idea that 'top of mind' awareness should be measured. Investigating intention profiles can help inform campaign impacts and continuation intentions.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".