Chronic pain patients' willingness to share personal identifiers on the web for the linkage of medico‐administrative claims and patient‐reported data: The chronic pain treatment cohort
Bibliographic record
Abstract
PURPOSE: The linkage between patient-reported data and medico-administrative claims is of great interest for epidemiologic research. The goal of this study was to assess the willingness of people living with chronic pain to share personal identifiers on the web for the linkage of medico-administrative and patient-reported data. METHODS: This methodological investigation was achieved in the context of the implementation of the chronic pain treatment (COPE) cohort. A web-based recruitment initiative targeting adults living with chronic pain was conducted in the province of Quebec (Canada). RESULTS: A total of 1935 participants completed the questionnaire (mean age: 49.86 ± 13.27; females: 83.69%), 921 (47.60%) of which agreed to data linkage and shared their personal identifiers (name, date of birth, health insurance number online). The most common reasons for refusal were: (1) concerns regarding data security/privacy (25.71%) and (2) the belief that the requested data were too personal/intrusive (13.52%). Some participants did not understand the relevance of data linkage (11.81%). Participants from the COPE cohort and those from the subsample who agreed to data linkage were comparable to other random samples of chronic pain individuals in terms of age and pain characteristics. CONCLUSIONS: Although approximately half of the participants refused data linkage, our approach allowed for the implementation of a data platform that contains a diverse and substantial sample. This investigation has also led to the formulation of recommendations for web-based data linkage, including placing items designed to assess willingness to share personal identifiers at the end of the questionnaire, adding explanatory videos, and using a mixed-mode questionnaire.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".