Psychometric Properties of the OSPRO–YF Screening Tool in Patients with Shoulder Pathology
Bibliographic record
Abstract
Purpose: The Optimal Screening for Prediction of Referral and Outcome Yellow Flag (OSPRO–YF) is a screening tool that incorporates many important psychosocial domains into one questionnaire to reduce the burden of completing multiple questionnaires. The objectives of this study were to examine the reliability and validity of the 10-item version of the OSPRO–YF with patients with shoulder conditions. Method: The study group consisted of injured workers with an active compensation claim for a shoulder injury. The control group consisted of patients with a complaint of shoulder pain but without a work-related shoulder injury. We examined reliability (internal consistency, test–retest) and validity (factorial, convergent, known groups). The Hospital Anxiety and Depression Scale; the Quick Disabilities of Arm, Shoulder and Hand; and the short Örebro Musculoskeletal Pain Screening Questionnaire were used for comparison. Results: Eighty patients had an active compensation claim, and 160 were in the control group. The intra-class correlation coefficient values for two observations of the domain scores varied from 0.91 to 0.94. The test–retest reliability of the dichotomous constructs was moderate to perfect for 8 of 11 constructs. The 10-item OSPRO–YF questionnaire had three distinct domains, as conceptualized by the developers: mood, fear avoidance, and positive affect–coping. The Cronbach’s a coefficients for these domains were 0.88, 0.94, and 0.94, respectively. The associations between the psychological constructs and domains and the similar theoretically derived scales were moderate to high and in the expected direction. Of the 11 constructs of the OSPRO–YF, 10 differentiated between patients with and without a work-related injury ( p-values ranging from 0.028 to < 0.001). Conclusions: The 10-item OSPRO–YF reduces the burden of using multiple questionnaires and has acceptable test–retest and internal consistency reliability and factorial, convergent, and known-groups validity.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".