Social marketing approach to understanding what adolescents need in a community-based healthy lifestyle intervention program
Bibliographic record
Abstract
Background: Overweight and obesity affects almost 30% of Canadian children and adolescents aged 2-17 years old which can lead to chronic disease later on in life. Research shows that healthy weight programs are effective at reducing BMI but have issues regarding recruitment and retention. One way to address these problems is by using a Social Marketing framework to determine what adolescents need in a community-based healthy weight program. Methods: Open-ended and closed-ended question surveys were conducted with multiple perspectives including youth aged 13-17 years, parents, and youth workers in Fall 2018. Open-ended question answers were a priori categorized by the ‘4Ps’ of the SM framework (Product, Price, Place, and Promotion) while frequency count data was generated for closed-ended question answers. Open-ended answer data were managed using NVivo 12 and were analyzed using Braun and Clarke’s six-step approach to thematic analysis (Braun & Clarke, 2006). Results: A ‘marketing mix’ was thematically generated to identify elements of a healthy weight program that adolescents need in order to participate from all three perspectives. Based on the findings, programs should include physical activity, nutrition, and emotional/social health components that are relevant and fun (Product). They should also emphasize benefits to participating such as improvement to physical and mental health, having fun, receiving incentives, and building relationships (Product) while minimizing barriers including emotional health concerns, lack of time, financial cost, transportation, boring programs (Price). Differences were found amongst perspectives in terms of types of incentives, transportation, and cost of program. Programs should take place in convenient, appealing, and safe locations that may already exist including schools or recreation centres (Place) and should also be promoted using social media and peer word-of-mouth or create partnerships with youth-relevant organizations and use body positive language (Promotion). Conclusion: Using this foundational work of a ‘marketing mix’ can help program developers design programs that will help recruit and retain youth in community-based healthy weight programs. Elements of social marketing were not considered in this study including competition, segmentation, and branding which further highlights the need for exploring competing behaviours in youths’ lives, different priority audience segments of BC, and brands that can be used to recruit and retain youth.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".