Physician Attributes That Matter Most: Results from a Qualitative Inquiry of Oncologists, Patients Receiving Oncological Care, and Medical Students
Bibliographic record
Abstract
BACKGROUND: Physician attributes significantly impact patient outcomes, satisfaction, and trust. Various attribute frameworks have been developed to help structure and guide undergraduate medical education and subsequent clinician practice; however, prioritization of these attributes vary by stakeholder (patients, physicians, medical students). Based on findings from two previous studies completed by the research team, we sought to understand the context in which individuals in these stakeholder groups prioritize particular physician attributes. We adopted a qualitative approach, conducting semi-structured interviews with patients (N = 11), doctors (N = 11), and medical students (N = 12), for a total sample of 34. RESULTS: Thematic analysis of data resulted in the following five themes: caring, communicator, expert, professional, curiosity and open-mindedness. Central to our findings was the need for a positive, trusting provider-patient relationship, which was framed as the conduit to quality patient care (both receiving and providing). The attributes believed to support this central finding differed, noting that "caring", "curiosity and open-mindedness" are not typical in physician attribute frameworks. Findings suggest there is a central guiding philosophy shaping what medical students, physicians and patients alike, need in the context of the provider-patient relationship, which transcends particular attributes. The guiding philosophy of relational inquiry is used to further situate study findings. CONCLUSIONS: Integrating a central guiding philosophy can add additional depth and nuance to attribute frameworks, ensuring considerations for qualities that transcend particular attribute characteristics, such as "caring" and "curiosity and open-mindedness" are also explicitly used to help structure and guide undergraduate medical education and subsequent clinician practice.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".