Factors influencing the adoption of a healthy eating campaign by federal cross-sector partners: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The Eat Well Campaign (EWC) was a social marketing campaign developed by Health Canada and disseminated to the public with the help of cross-sector partners. The purpose of this study was to describe factors that influenced cross-sector partners' decision to adopt the EWC. METHODS: Thematic content analysis, based primarily on an a priori codebook of constructs from Roger's diffusion of innovations decision process model, was conducted on hour-long semi-structured telephone interviews with Health Canada's cross-sector partners (n = 18). RESULTS: Dominant themes influencing cross-sector partners' decision to adopt the EWC were: high compatibility with the organization's values; being associated with Health Canada; and low perceived complexity of activities. Several adopters indicated that social norms (e.g., knowing that other organizations in their network were involved in the collaboration) played a strong role in their decision to participate, particularly for food retailers and small organizations. The opportunity itself to work in partnership with Health Canada and other organizations was seen as a prominent relative advantage by many organizations. Adopters were characterized as having high social participation and positive attitudes towards health, new ideas and Health Canada. The lack of exposure to the mass media channels used to diffuse the campaign and reserved attitudes towards Health Canada were prominent obstacles identified by a minority of health organizations, which challenged the decision to adopt the EWC. Most other barriers were considered as minor challenges and did not appear to impede the adoption process. CONCLUSIONS: Understanding factors that influence cross-sector adoption of nutrition initiatives can help decision makers target the most appropriate partners to advance public health objectives. Government health agencies are likely to find strong partners in organizations that share the same values as the initiative, have positive attitudes towards health, are extremely implicated in social causes and value the notion of partnership.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".