Motivational impact, self-reported fear, avoidance, and harm perception of shoulder movements pictures in people with chronic shoulder pain
Bibliographic record
Abstract
Objectives (1) to represent in pictures a group of items from the International Classification of Functioning (ICF) that represent daily situations; (2) to compare valence and arousal evoked by these pictures between chronic shoulder pain and pain-free control groups and assess self-reports of fear, avoidance, and harm perception.Methods This is a cross-sectional observational study and approved by Research Ethics Committee. Selected pictures representing items from the ICF were judged by members of the general public using an online form. We used the set of International Affective Picture System and the Self-Assessment Manikin to compare valence and arousal between groups. The chronic shoulder pain group answered questions regarding self-reports of fear, avoidance, and harm perception.Results The protocol consisted of 58 pictures. A repeated measures ANOVA for valence revealed a main effect of group, F(1, 9)=24.81;p < 0.005, and no effect on the arousal, F(1,9)=2.00;p < 0.190. The shoulder pain group judged shoulder pictures more aversive. The picture that represents the movement of carrying on shoulder presented the highest medians of self-reports of fear, avoidance, and harm perception 10(8-10).Conclusion The pictures represent daily activities that correspond to the items from the ICF. The valence of the shoulder pictures was different between the groups, shoulder pictures were considered more aversive for the group with chronic shoulder pain. The responses of avoidance, fear, and harm perception showed higher median in the carrying on shoulders, hip, and back picture.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".