Evaluating the performance of the Pain Interference Index and the Short Form McGill Pain Questionnaire among Chilean injured working adults
Bibliographic record
Abstract
BACKGROUND: Chronic pain can lead to economic instability, decreased job productivity, and poor mental health. Therefore, reliable identification and quantification of chronic pain is important for clinical diagnosis and treatment. OBJECTIVE: To determine the psychometric properties of the Spanish language versions of the Pain Interference Index (PII) and the Short Form McGill Pain Questionnaire (SF-MPG) among a population of working adults who experienced injury in Santiago, Chile. METHODS: A total of 1,975 participants with work-related injuries were interviewed to collect sociodemographic, occupational, and chronic pain characteristics. Construct validity and factorial structure of the PII and SF-MPG were assessed through exploratory factor analyses (EFA). Cronbach's alpha was used to evaluate internal consistency. RESULTS: The PII mean score was 3.84 ± 1.43 among all participants. The SF-MPG median score was 11 [IQR: 6-16] in this study population. Cronbach's alpha for the PII was 0.90 and 0.87 for the SF-MP. EFA resulted in a one factor solution for the PII. A two-factor solution was found for the SF-MPG. The two-factors for SF-MPG were sensory and affective subscales with Cronbach's alpha of 0.82 and 0.714, respectively. When the two scales were combined, an EFA analysis confirmed the PII and SF-MPG measure different aspects of chronic pain. CONCLUSIONS: The PII and SF-MPG had good construct validity and reliability for assessing different aspects of chronic pain among working Chilean adults.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".