Creative Strengths-Based Approaches to Health Promotion: Perspectives From Graduate Training Experiences
Bibliographic record
Abstract
The authors met during a career development experience where they discussed the commonalities of their successes and challenges conducting creative strengths-based health promotion research with underserved communities during their graduate and postgraduate training. They identified changes to health promotion pedagogy that they would like to see in the future. These include understanding both the strengths and the challenges of creative strengths-based health promotion research conducted with underserved communities, ensuring that reflexivity and flexibility is a component of the process, developing support networks for trainees, understanding personal limitations to effect change, and supporting self-care. They hope that trainees and health education programs will learn from their experiences.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".