Procedures for assessing psychological predictors of injuries in circus artists: a pilot prospective study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Research on psychological risk factors for injury has focused on stable traits. Our objective was to test the feasibility of a prospective longitudinal study designed to examine labile psychological states as risk factors of injury. METHODS: We measured psychological traits at baseline (mood, ways of coping and anxiety), and psychological states every day (1-item questions on anxiety, sleep, fatigue, soreness, self-confidence) before performances in Cirque du Soleil artists of the show "O". Additional questions were added once per week to better assess anxiety (20-item) and mood. Questionnaires were provided in English, French, Russian and Japanese. Injury and exposure data were extracted from electronic records that are kept as part of routine business practices. RESULTS: The 43.9% (36/82) recruitment rate was more than expected. Most artists completed the baseline questionnaires in 15 min, a weekly questionnaire in <2 min and a daily questionnaire in <1 min. We improved the formatting of some questions during the study, and adapted the wording of other questions to improve clarity. There were no dropouts during the entire study, suggesting the questionnaires were appropriate in content and length. Results for sample size calculations depend on the number of artists followed and the minimal important difference in injury rates, but in general, preclude a purely prospective study with daily data collection because of the long follow-up required. However, a prospective nested case-crossover design with data collection bi-weekly and at the time of injury appears feasible. CONCLUSION: A prospective study collecting psychological state data from subjects who train and work regularly together is feasible, but sample size calculations suggest that the optimal study design would use prospective nested case-crossover methodology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it