Exercise is medicine: critical considerations in the qualitative research landscape
Bibliographic record
Abstract
Since the American Medical Association and the American College of Sports Medicine partnered to launch Exercise is Medicine® (EIM) in 2007, the program has gained traction in 43 countries. The EIM discourse has been fruitful for framing exercise/physical activity as a form of disease prevention and/or symptom management for chronic conditions and mental health. This editorial ‘sets the stage’ for the articles within the special issue that coalesce a critical inquiry dialogue on EIM, by outlining taken for granted assumptions inherent in EIM. Assumptions include that people’s inactivity (and poor health) necessitates quick/planned intervention, exercise is positive/good for everyone and that the connection of exercise to medicine enhances credibility. Assumptions are problematized through grounding them in a neoliberal discourse of healthism, which emphasizes individual responsibility and/or experts as gatekeepers and facilitators of risk management through exercise. Three challenges to each of the assumptions are offered to explore EIM as socially, culturally and politically constructed, expanding the critical EIM dialogue. An overview of each of the articles within the special issue is then outlined to show ‘examples in use’ of critical theories and methodologies grounded broadly in interpretivist forms of inquiry and social constructionism. We conclude with noting the impetus and goal of this special issue--to spark further interest, dialogue and critical qualitative research on EIM –bringing forward the personal, socio-cultural, political iterations and potential of EIM.
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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 | 0.516 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.033 | 0.128 |
| Scholarly communication | 0.041 | 0.033 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".