Development and content validity of a rating scale for the Pain and Disability Drivers Management Model.
Bibliographic record
Abstract
Rationale, aims and objectives Establishing the biopsychosocial profile of patients with low back pain is essential to personalize care. The Pain and Disability Drivers Management model (PDDM) has been suggested as a useful framework to help clinicians establish the profile. Yet, there is no tool to facilitate its integration into clinical practice. Thus, the aim of this study is to develop and validate a rating scale, in order to rapidly establish the patient’s profile based on the domains of the PDDM. Method The tool was developed in accordance with the principles of COSMIN methodology. We conducted 3 steps: 1) item generation from a comprehensive review, 2) refinement of the scale with clinicians’ feedback, and 3) statistical analyses to assess the content validity. To validate the item assessing with Likert scales, we performed Item level-Content Validity Index (I-CVI) analyses on three criteria with an a priori threshold of >0.78. We conducted Average-Content Validity Index (Ave-CVI) analyses to validate the overall scale with a threshold of >0.9. Results Coherent with the PDDM, we developed a 5-item rating scale with 4 score options. We selected clinical instruments to screen the presence or absence of the categories of each domain. 42 participants provided feedback to refine the clarity, presentation and clinical applicability of the scale. The statistical analysis of the latest version presented I-CVI above the threshold for each item (between 0.94 and 1). The analysis of the overall scale supported its validation (Ave-CVI=0.95 [0.94;0.97]). Conclusion From the 51 biopsychosocial elements contained within the 5 domains of the PDDM, we developed a rating scale that allows to rapidly screen for problematic issues for categories within each domain. The involvement of clinicians in the process allowed us to validate the content of the first scale to establish the patient’s biopsychosocial profile for people with LBP
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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 | 0.040 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".