French-Canadian translation of a self-report questionnaire to monitor opioid therapy for chronic pain: The Opioid Compliance Checklist (OCC-FC)
Bibliographic record
Abstract
Context: Chronic noncancer pain (CNCP) is a frequent condition among Canadians. The psychosocial and economic costs of CNCP for individuals, their families, and society are substantial. Though opioid therapy is often used to manage CNCP, it is also associated with risks of misuse. The Opioid Compliance Checklist (OCC) was developed to monitor opioid misuse in patients taking opioids for CNCP. The objective of the present study was to provide a French-Canadian translation of the eight-item OCC, the OCC-FC.Methods: The eight-item OCC was translated for use in Québec using published guidelines for the translation and adaptation of self-report measures, including an expert committee and a double forward–backward translation process. A pretest of the adapted eight-item OCC was also conducted among 30 patients with CNCP.Results: A French-Canadian version of the OCC was generated. When ambiguity in the items was detected during expert committee consultation or pretest administration, modifications made were kept to a strict minimum to facilitate future comparisons across studies using the original English and translated French-Canadian version.Discussion: This study provides a culturally adapted tool that will contribute to identifying French-Canadian patients with CNCP who misuse opioids over the course of opioid therapy. This translation of the OCC has the strong potential to be useful in research and clinical settings.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.015 | 0.003 |
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".