A comparison of patient and physician attributes that promote patient involvement in treatment decision making in the oncology consultation
Bibliographic record
Abstract
6098 Background: Cancer patients have indicted a desire to be more involved in treatment decision making (TDM). However, little is known about the attributes of patients, physicians and their interaction that promotes patient involvement in TDM in the oncology consultation. This study compared attributes generated by patients and physicians that make it easier for patients to be involved in TDM. Methods: Semi-structured interviews were undertaken with 19 patients with cancer (lung, breast, prostate, GI) and 21 medical and radiation oncologists at a regional cancer centre. Participants were asked to identify attributes of physicians, patients and their interaction that promotes patient involvement in TDM. Interview transcripts were independently coded by 2 analysts using decision rules to identify specific attributes. Attributes identified by each analyst were compared and a high level of agreement was found. The analysts then independently compared the physician and patient generated lists and identified common vs unique items. There was a high level of agreement on which attributes were common to both lists versus unique. Results: Oncologists identified 173 physician, 59 patient and 9 interaction items. Patients identified 50 physician, 42 patient and 11 interaction items. Patients and physicians identified 17 common physician items, 29 common patients items and 1 common interaction item. Physicians identified 138 more attributes than patients, most of which were physician related. Common patient attributes centred on information seeking (eg prepare for the consultation by reading, be aware of all treatment options and question the options). Common physician attributes focused on specific communication behaviors (eg, make eye contact, tailor information to patient needs, be direct with patients, ensure patient understands information). The common interaction item was to keep the discussion informal. Conclusions: Patients and physicians appear to have different ideas about what is important to promote patient involvement in TDM. Many of the attributes identified can be easily incorporated into current practice. There is a need to develop and evaluate communication skills training to promote patient involvement in TDM. No significant financial relationships to disclose.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".