Designing and implementing a positive body image program: Unchartered territory with a diverse team of participants
Bibliographic record
Abstract
This article highlights the use and importance of action research in creating a new positive body image program. The purpose of the larger action research project was to design, test, and implement a positive body image program by working with a core group of diverse stakeholders from an exercise facility. Stakeholders included older adults (aged 55+), people with physical disabilities, and those with cardiovascular disease or risk factors, populations rarely included in the body image literature, particularly in program design. The resulting program was built to teach members of the facility about body image (e.g. its definition, causes, and influences), positive body image, and how to manage their own body image experiences and be critical of the Western beauty ideal. The project is outlined with emphasis on the development of the program along with the researcher’s reflexive notes and participant feedback. We also highlight the strengths and challenges of using action research in the development of a positive body image program with suggestions to improve this process for future action researchers. This research highlights the importance of using action research in order to engage participants who are not typically involved in the knowledge production process of body image program development.
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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 | 0.075 | 0.052 |
| 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 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".