032 Shared decision-making in patients with chronic conditions among the French e-cohort compare: an online cross-sectional survey
Bibliographic record
Abstract
Patients living with chronic conditions face many decision-making challenges during their lifetime with their disease. We aimed to assess the level of Shared Decision-Making (SDM) perceived by patients with chronic conditions in France. A cross-sectional survey was conducted in ComPaRe, a nationwide e-cohort of adult patients with chronic conditions. Data were weighted to represent patients with at least one chronic condition in France. We asked participants how they perceived the level of SDM for the most important health decision taken in the past 12 months by using the French version of the 9-item Shared Decision- Making Questionnaire (SDM-Q-9), ranging from 0 to 100. We described the level of SDM and studied factors associated with. From April 6 to May 18, 2023, 2097 participants answered the survey (73.0% were female, median (IQR) age was 51 (38–61) and 71.0% had a high education level. The mean (+/-SD) SDM-Q-9 score was 63 ± 27 with the highest score for decisions regarding cancer (72+/-25) and, the lowest for dermatological conditions, (60+/-27). Mean SDM-Q-9 was also higher among men (73+/-27) than women (62 +/- 27) were. Disparities also emerged regarding the type of decision, with higher SDM-Q- 9 levels for decisions regarding surgery (71+/-25) compared to drug related decisions (60+/-28). Multivariable analysis retrieved that being male, employed, having cancer, a high level of health literacy, perceiving lower treatment burden and taking decisions about surgery were associated with higher SDM-Q-9 level. French patients with chronic diseases perceived a high level of SDM but we identified disparities in SDM level depending on gender, chronic disease or type of decision. It is the first nationwide population-based survey to evaluate the level of SDM among patients with chronic diseases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".