Effective Social Marketing to Promote a Campus-Based Physical Activity Intervention: Students' Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it