Exploring participants’ perspectives on adverse events due to resistance training: a qualitative study
Bibliographic record
Abstract
The objective of this study was to explore the experiences and perspectives of individuals with chronic health conditions who had an adverse event (AE) as a result of resistance training (RT). We conducted web conference or telephone-based one-on-one semi-structured interviews with 12 participants with chronic health conditions who had an AE as a result of RT. Interview data were analyzed using the thematic framework method. Six themes were identified: (1) personal experiences with aging influence perceptions of RT; (2) physical and emotional consequences of AEs limit activities and define future RT participation; (3) injury recovery defines the severity of AE; (4) health conditions influence the perceived risks and benefits of participating in RT; (5) RT setting and trained supervision influence exercise behaviors and risk perceptions; and (6) experiencing a previous AE influences future exercise behavior. Despite participant awareness of the value and benefits of RT in both the context of aging and chronic health conditions, there is concern about experiencing exercise-related AEs. The perceived risks of RT influenced the participants’ decision to engage or return to RT. Consequently, to promote RT participation, the risks, not just the benefits, should be properly reported in future studies, translated, and disseminated to the public. Novelty: –To increase the quality of published research with respect to AE reporting in RT studies. –Health care providers and people with common health conditions will be able to make evidence-based decisions as to whether the benefits of RT truly outweigh the risks.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".