Effective Social Marketing to Promote a Campus-Based Physical Activity Intervention: Students' Perspectives
Bibliographic record
Abstract
Social marketing has the potential to increase knowledge of preventive health issues and to elevate participation in health promotion programs (Bloch, 1984). Health promoters would be wise to utilize social marketing principles and strategies for promoting programs because this could bring forth more cost-effective programs that reach a wider segment of the target audience. Lefebvre and Flora (1988) argued that it is the target population's needs and input, in as many areas as possible, that are the essential foci throughout all phases of the social marketing process. Conducting audience analysis garners information concerning the needs, demographics, and preferences of the specific target population (Blair, 1995; Lefebvre & Flora, 1988). Consequently, this study explored methods for the effective social marketing of a physical activity intervention for university students, specifically a buddy system and record-keeping device.\nA heterogeneous sample of 65 undergraduate students from the University of Western Ontario (UWO) participated in 13 focus groups. Data collection and analysis took place simultaneously using a combination of the editing and template organizing styles outlined by Miller and Crabtree (1999). Two researchers independently conducted inductive content analysis on each transcript and compared their findings. Many strategies were used to ensure trustworthiness of the data, as outlined by Guba and Lincoln (1989). NVivo software was used to code and categorize emerging themes. The University of Western Ontario Academic Development Fund funded this project and ethical approval was obtained through The University of Western Ontario.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".