Association between perception of harm and valence of shoulder movement images with disability levels related to chronic shoulder pain
Bibliographic record
Abstract
ABSTRACT: Recent studies highlight an interplay between pain perception and emotional responses. This necessitates a thorough investigation into how beliefs and motivational influences respond to visual stimuli of movements. Such an analysis is crucial for understanding the extent to which these factors contribute to disability levels associated with shoulder pain. We aimed to investigate the relationship between the perception of harm and the valence in images depicting shoulder movements and determine how these perceptions are linked to disability levels associated with shoulder pain. This cross-sectional study recruited 42 individuals with chronic shoulder pain. Participants were presented with 58 shoulder movements images. Each participant evaluated these images for emotional valence and arousal using the self-assessment manikin. For every image, they provided their level of avoidance, fear, and perception of harm in a numerical scale. We measured disability levels and pain catastrophizing using the Shoulder Pain and Disability Index and the Pain Catastrophizing Scale. A direct acyclic graph was used. Multiple linear regression analysis was conducted with shoulder disability as the dependent variable and perception of harm and valence as independent variables, adjusted for the confounders catastrophizing and arousal. This analysis resulted in a significant model ( F4,37 = 11.44; adjusted R2 = 0.547; P < 0.01). The perception of harm to shoulder movement (β = 0.11; P < 0.001; 95% confidence interval = 5.6-11.8) was significantly associated with the level of shoulder disability, whereas valence did not show a significant association (β = 0.26; P = 0.15; 95% confidence interval = 1.7-10.8). The perception of harm associated with shoulder movements images during daily activities was associated with disability. Individuals who believe that shoulder movements are harmful have greater disability.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".