Facilitators and barriers experienced by federal cross-sector partners during the implementation of a healthy eating campaign
Bibliographic record
Abstract
OBJECTIVE: To identify facilitators and barriers that Health Canada's (HC) cross-sector partners experienced while implementing the Eat Well Campaign: Food Skills (EWC; 2013-2014) and describe how these experiences might differ according to distinct partner types. DESIGN: A qualitative study using hour-long semi-structured telephone interviews conducted with HC partners that were transcribed verbatim. Facilitators and barriers were identified inductively and analysed according partner types. SETTING: Implementation of a national mass-media health education campaign. SUBJECTS: Twenty-one of HC's cross-sector partners (food retailers, media and health organizations) engaged in the EWC. RESULTS: Facilitators and barriers were grouped into seven major themes: operational elements, intervention factors, resources, collaborator traits, developer traits, partnership factors and target population factors. Four of these themes had dual roles as both facilitators and barriers (intervention factors, resources, collaborator traits and developer traits). Sub-themes identified as both facilitators and barriers illustrate the extent to which a facilitator can easily become a barrier. Partnership factors were unique facilitators, while operational and target population factors were unique barriers. Time was a barrier that was common to almost all partners regardless of partnership type. There appeared to be a greater degree of uniformity among facilitators, whereas barriers were more diverse and unique to the realities of specific types of partner. CONCLUSIONS: Collaborative planning will help public health organizations anticipate barriers unique to the realities of specific types of organizations. It will also prevent facilitators from becoming barriers. Advanced planning will help organizations manage time constraints and integrate activities, facilitating implementation.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".