Shared decision making in oncology: assessing oncologist behaviour in consultations in which adjuvant therapy is considered after primary surgical treatment
Bibliographic record
Abstract
INTRODUCTION: Shared decision making (SDM) is now considered a desirable goal in health care, yet little is known about current practice in cancer care, and its impact on patient outcomes. This study aimed to develop an oncology-specific coding system for SDM, explore variations in SDM according to patient and disease characteristics, determine the relationship between SDM and patient satisfaction with the consultation, and explore the impact of SDM on patient anxiety. METHODS: Sixty-three medical and radiation oncology consultations with patients with primary cancer involving consideration of adjuvant therapy after surgery were audio-taped, transcribed and coded. Intra and inter-rater reliability of the coding system was 95 and 90% respectively. Patients completed questionnaires before and after the consultation. RESULTS: Construct validity of the SDM coding system was successfully conducted. Oncologists demonstrated on average under 11 of 18 SDM behaviours. Behaviours seeking patient preferences were particularly rare. SDM behaviours were more apparent in consultations involving female breast cancer patients. SDM behaviour scores in combination with patient involvement preference could predict achievement of patient involvement preference but not overall patient satisfaction. Although there was no overall relationship between patient anxiety and SDM scores, it did appear that physicians may change SDM behaviour according to patient factors including anxiety. CONCLUSION: Our findings reinforce the importance of the doctor in facilitating shared decision making in oncology consultations.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".