Researchers’ perspectives on adverse event reporting in resistance training trials: a qualitative study
Bibliographic record
Abstract
The objectives of our study were to understand researchers' current practices and perspectives on adverse event (AE) reporting in clinical trials of resistance training (RT) and to identify barriers and facilitators of AE reporting. We conducted web conference or telephone-based one-on-one semistructured interviews with 14 researchers who have published RT studies. We audio-recorded and transcribed the interviews and analyzed the data using the thematic framework method. Four themes were identified: (1) researchers lack guidance and/or motivation for rigorous AE reporting; (2) researchers who undertake AE reporting educate and value participants, use trained personnel, and implement standardized guidelines; (3) suboptimal implementation of existing AE reporting standards and the perception that available guidelines do not apply to exercise trials; and (4) acceptability and feasibility of an exercise-specific guide for AE reporting depend on its content and format. In conclusion, AE reporting methods in the field of exercise science do not align with best practice. Strategies to reduce inconsistent and suboptimal AE reporting in RT trials are urgently needed and could be based on the barriers and facilitators identified in this study.
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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 | 0.257 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| 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; 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".