The Chronic Pain Myth Scale: Development and Validation of a French-Canadian Instrument Measuring Knowledge, Beliefs, and Attitudes of People in the Community towards Chronic Pain
Bibliographic record
Abstract
Background. In order to better design awareness programs on chronic pain (CP), measurement of knowledge, beliefs, and attitudes of people in the community towards this condition is most useful.Objectives. To develop and validate a French-Canadian scale that could be used for this purpose.Methods. Items of the Chronic Pain Myth Scale (CPMS) were developed based on different information sources, reviewed by pain experts, and pretested. The CPMS was administered to 1555 participants among the general Quebec population.Results. The final CPMS contained 26 items allowing the calculation of three subscales scores (knowledge, beliefs, and attitudes towards people suffering from CP, biopsychosocial impacts of CP, and treatment of CP) which showed adequate internal consistency (α= 0.72–0.82). There were statistically significant differences in subscales scores between participants who reported suffering versus not suffering from CP, reported knowing versus not knowing someone who suffers from CP, and reported being versus not being a healthcare professional, which supports the construct validity of the scale.Conclusions. Our results provide preliminary evidence supporting the psychometric qualities of the use of the CPMS for the measurement of knowledge, beliefs, and attitudes towards CP among French-speaking individuals of the Quebec general population.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".