Testing the hierarchy of effects model: ParticipACTION's serial mass communication campaigns on physical activity in Canada
Bibliographic record
Abstract
The hierarchy of effects (HOE) model is often used in planning mass-reach communication campaigns to promote health, but has rarely been empirically tested. This paper examines Canada's 30 year ParticipACTION campaign to promote physical activity (PA). A cohort from the nationally representative 1981 Canada Fitness Survey was followed up in 1988 and 2002-2004. Modelling of these data tested whether the mechanisms of campaign effects followed the theoretical framework proposed in the HOE. Campaign awareness was measured in 1981. Outcome expectancy, attitudes, decision balance and future intention were asked in 1988. PA was assessed at all time points. Logistic regression was used to sequentially test mediating and moderating variables adjusting for age, sex and education. No selection bias was observed; however, relatively fewer respondents than non-respondents smoked or were underweight at baseline. Among those inactive at baseline, campaign awareness predicted outcome expectancy which in turn predicted positive attitude to PA. Positive attitudes predicted high decision balance, which predicted future intention. Future intention mediated the relationship between decision balance and sufficient activity. Among those sufficiently active at baseline, awareness was unrelated to outcome expectancy and inversely related to positive attitude. These results lend support to the HOE model, in that the effects of ParticipACTION's serial mass media campaigns were consistent with the sequential rollout of its messages, which in turn was associated with achieving an active lifestyle among those initially insufficiently active. This provides support to an often-used theoretical framework for designing health promotion media campaigns.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".