Using the Awareness, Motivation, Skills, and Opportunity Framework for Health Promotion in a Primary Care Network
Bibliographic record
Abstract
Purpose. To use the Awareness, Motivation, Skills, and Opportunity (AMSO) framework as a foundation for service delivery in a primary care network (PCN). Method. The AMSO framework (awareness, motivation, skills, opportunity) was integrated into PCN program design: Health Basics (8 weeks with monthly follow-up) focused on healthy living and Happiness Basics (6 weeks) used positive psychology. Evaluation included quality of life (QofL) and participant experience; weight, body mass index, and waist circumference were included for Health Basics. Data were analyzed using descriptive statistics, paired t tests, and thematic analysis. Setting. PCN in western Canada with midsized urban center and surrounding areas. Participants. Health Basics—adults with or at risk for chronic disease (n = 103). Happiness Basics—adults with depression, languishing, or flourishing (n = 124). Results. Changes were evident in weight loss, body mass index, waist circumference, and QofL (p < .05) for Health Basics participants. The participants also reported being more active, eating healthier, having a more positive mind-set, and having confidence in making lifestyle changes. Happiness Basics participants’ QofL improved in all domains (p < .05) except the physical summary score (p = .079). Happiness participants described positive experiences and learned new skills. Conclusion. The AMSO framework was successfully implemented in our PCN. Recommendations are included to improve program effectiveness and use.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".