Professionalism, professionalization, expertise and compassion: a qualitative study of medical residents
Bibliographic record
Abstract
BACKGROUND: Formal and informal medical curricula convey expectations about professionalization, that is, the development of physician identity, and also about professionalism. This study examined whether, in general, junior residents experienced any dissonance between these roles and focused particularly on how they negotiated conflicts between compassion, self-care, duty and medical expertise. METHODS: In 2015, purposive sampling was used to select 21 first-year residents at a Canadian medical school. Participants listened to a 5-min audio-recording narrated in either male or female voice. Facing compassion fatigue after three obstetrical disasters over less than 2 days the resident narrator asks to go home. Participants reacted in writing to questions about this request and relevant teaching/modelling. Responses were analyzed using a qualitative, exploratory, thematic research design. RESULTS: Four themes were identified: i) empathy, self-doubt and fear of weakness, ii) the need for support from and communication with physicians and others, iii) education received, and iv) professionalization outranks professionalism. Participants agreed that under the circumstances the narrator's care, compassion and request were appropriate. Never the less, many grappled with feeling that asking to be relieved of work demonstrated weakness and a shirking of responsibility. Respondents had received no formal teaching about balancing compassion for patients or self with professional duty. Preceptors' informal teaching and modeling valorized scientific disengagement above all else. What emerged was participants' drive to become detached clinicians who set aside emotional responses and interactions that could impede and be incompatible with professionalization. However, participants also recognized and lamented what was lost in such a transformation. CONCLUSION: In the transition from student to practitioner, trainees' views and the modeling they receive shift emotion and compassion, whether for self or patients, from assets to liabilities as they aim to be invincible medical experts.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".