What Attributes Matter Most in Physicians? Exploratory Findings from a Single-Centre Survey of Stakeholder Priorities in Cancer Care at a Canadian Academic Cancer Centre
Bibliographic record
Abstract
BACKGROUND: Limited research exists regarding how healthcare stakeholders prioritize the importance of differing physician attributes in oncologists. Identifying these priorities can help ensure that Canadian cancer care continues to meet the needs of its patients. In our previous research, compassion and empathy were identified as important physician attributes, with answers like knowledge, professionalism or communication less common. We hypothesized that respondents may have been assuming other, underlying qualities in their oncologists when they prioritized "compassion" and "empathy". To test this, the current study asks respondents to rank important physician attributes. METHODS: With ethics approval, we asked healthcare stakeholders (physicians, nurses, patients, caregivers, medical students, and allied healthcare providers) to rank the eight most popular qualities or attributes. We identified differences between which characteristics each group valued most in physicians. RESULTS: 375 respondents participated in the survey. "Knowledge" and "competence" were the most popular answers in the current study among all groups except medical students. CONCLUSION: Previously, we identified compassion as a highly valued attribute; however, this survey suggests that this may be with the assumption that a physician is knowledgeable and competent. Future research will use semi-structured interviews to investigate respondents' rationales for making their choices and help interpret our findings in this study.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".