Cancer patients’ preferred and perceived level of involvement in treatment decision-making: an epidemiological study
Bibliographic record
Abstract
Background: We aimed to analyze preferred and perceived levels of patients’ involvement in treatment decision-making in a representative sample of cancer patients. Material and Methods: We conducted a multicenter, epidemiological cross-sectional study with a stratified random sample based on the incidence of cancer diagnoses in Germany. Data were collected between January 2008 and December 2010. Analyses were undertaken between 2017 and 2019. We included 5889 adult cancer patients across all cancer entities and disease stages from 30 acute care hospitals, outpatient facilities, and cancer rehabilitation clinics in five regions in Germany. We used the Control Preferences Scale to assess the preferred level of involvement and the nine-item Shared Decision-Making Questionnaire to assess the perceived level of involvement. Results: About 4020 patients (mean age of 58 years, 51% female) completed the survey. Response rate was 68.3%. About a third each preferred patient-led, shared, or physician-led decision-making. About 50.7% perceived high levels, about a quarter each reported moderate (26.0%) or low (24.3%) levels of shared decision-making. Sex, age, relationship status, education, health care setting, and tumor entity were linked to preferred and/or perceived decision-making. Of those patients who preferred active involvement, about 50% perceived high levels of shared decision-making. Conclusion: The majority of patients with cancer wanted to be involved in medical decisions. Many patients perceived a high level of shared decision-making. However, many patients’ level of involvement did not fit their preference. This study provides a solid basis for efforts to improve shared decision-making in German cancer care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".