Understanding the complexities of shared decision-making in cancer: a qualitative study of the perspectives of patients undergoing colorectal surgery
Bibliographic record
Abstract
BACKGROUND: Decisions leading up to surgery are fraught with uncertainty owing to trade-offs between treatment effectiveness and quality of life. Past studies on shared decision-making (SDM) have focused on the physician-patient encounter, with little emphasis on familial and cultural factors. The literature is scarce in surgical oncology, with few studies using qualitative interviews. Our objective was to explore the complexities of SDM within the setting of colorectal cancer (CRC) surgery. METHODS: An interdisciplinary team developed a semistructured questionnaire. Telephone interviews were conducted with CRC patients in the practice of 1 surgical oncologist. Data saturation was achieved and a descriptive thematic analysis was performed. RESULTS: We interviewed 20 patients before achieving data saturation. Three major themes emerged. First, family was considered as a crucial adjunct to the patient-provider dyad. Second, patients identified several facilitators to SDM, including a robust social support system and a competent surgical team. Although language was a perceived barrier, there was no difference in level of involvement in care between patients who spoke English fluently and those who did not. Finally, patients perceived a lack of choice and control in decision-making, thus challenging the very notion of SDM. CONCLUSION: Surgeons must learn to appreciate the role of family as a vital addition to the patient-provider dyad. Family engagement is crucial for CRC patients, particularly those undergoing surgical resection of late-stage disease. Surgeons must be aware of the uniqueness of decision-making in this context to empower patients and families.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".