Evaluation of the psychometric properties of the health care providers’ pain and impairment relationship scale (HC-PAIRS) in health professionals and university students from Chile and Colombia
Bibliographic record
Abstract
Background: Chronic back pain is a frequent and disabling health problem. There is evidence that ignorance and erroneous beliefs about chronic low back pain among health professionals interfere in the treatment of people who suffer from it. The Health Care Providers' Pain and Impairment Relationship Scale (HC-PAIRS) has been one of the most used scale to assess these misbeliefs, but no studies have been reported in Latin America. Method: We studied the factorial structure of the HC-PAIRS in health personnel and health sciences university students in two Latin American countries: Colombia (n = 930) and Chile (n = 190). Spain's data was taken of the original study of the Spanish version of the HC-PAIRS (171 Physiotherapy students). Additionally, the measurement invariance of this scale among Chile, Colombia and Spain was evaluated by calculating three nested models: configural, metric and scalar. We used a Confirmatory Factor Analysis (CFA) in both Latin American samples, with Maximum Likelihood Robust (MLR) estimation to estimate the parameters. For the final model in each sample, reliability was assessed with the Composite Reliability (CR) index, and to obtain the proportion of variance explained by the scale the Average Variance Extracted (AVE) was calculated. Results: The one-factor solution shows an acceptable fit in both countries after deleting items 1, 6, and 14. For the resulting scale, the CR value is adequate, but the AVE is low. There is scalar invariance between Chile and Colombia, but not between these two countries and Spain. Conclusions: HC-PAIRS is useful for detecting misconceptions about the relationship between chronic low back pain that would cause health personnel to give wrong recommendations to patients. However, it has psychometric weaknesses, and it is advisable to obtain other evidence of 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 | 0.003 | 0.000 |
| 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 | 0.000 | 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".