Promoting meal planning through mass media: awareness of a nutrition campaign among Canadian parents
Bibliographic record
Abstract
OBJECTIVE: To evaluate awareness of the Eat Well Campaign (EWC) among parents and assess perceptions about its effectiveness. DESIGN: Post-campaign evaluation study with a cross-section of parents recruited through random digit dialling. Participants completed an online survey about EWC awareness, its perceived effectiveness among parents and their meal planning practices (attitudes, behaviours and self-efficacy). SETTING: A federal mass-media campaign disseminated by Health Canada (2013-2014) to promote meal planning to Canadian parents. PARTICIPANTS: Parents (n 964) of children aged 2-12 years from all Provinces and Territories. RESULTS: Of respondents, 41 % (390/964) were aware of the campaign; Quebec City and rural Quebec had the highest rates of awareness, whereas Vancouver, Winnipeg and Toronto had the lowest. Awareness was greater among parents with lower income, basic education and French-speakers. Campaign intensity was significantly associated with greater odds of reporting positive attitudes towards the EWC and meal planning (P < 0·05). Campaign awareness was significantly associated with greater odds of believing that meal planning helps maintain a healthy diet (OR = 1·68, 95 % CI 1·03, 2·74) and planning meals (OR = 1·66, 95 % CI 1·03, 2·54), but not self-efficacy, in adjusted models. CONCLUSIONS: The present study is the first to evaluate an initiative that promoted meal planning with mass media. The EWC demonstrated evidence of success in terms of equitable access to a nutrition initiative by reaching lower-income and less-educated parents. Understanding behavioural factors among different segments of the population will be important to target appropriate audiences and develop tailored interventions that support healthy eating practices.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".