How clinicians integrate humanism in their clinical workplace—‘Just trying to put myself in their human being shoes’
Bibliographic record
Abstract
INTRODUCTION: Humanism has been identified as an important contributor to patient care and physician wellness; however, what humanism means in the context of medicine has been limited by opinion and a focus on personal characteristics. Our aim was to describe attitudes and behaviours that enable clinicians to integrate humanism within the clinical setting. METHODS: We conducted semi-structured individual interviews with ten clinical faculty to explore how they enact and experience humanism in patient care and clinical teaching. Interpretive description was used to analyze the data qualitatively. RESULTS: Humanism in medicine was described through five themes representing core attitudes and behaviours: whole person care, valuing, perspective-taking, recognizing universality, and relational focus. Whole person care involved recognizing the multiple dimensions of personhood and sensitivity to others' needs; valuing involved respecting and appreciating others; perspective-taking consisted of considering others' perspectives, suspending judgment, and listening; recognizing universality involved acknowledging the shared human condition, finding common ground, transcending roles, and humility; and relational focus was described through multiple relationships between patients, families, clinicians and learners, becoming part of another's story, reciprocal influence, and accompaniment. CONCLUSIONS: Whereas previous descriptions of humanism have focused on clinicians' personal qualities, our research describes a number of attitudinal and behavioural foundations of humanistic care and teaching, grounded in the experiences of clinical faculty. In drawing attention to the holistic and relational elements of humanism, our work highlights how these foundational elements can be more explicitly integrated into patient care, workplace culture, and clinical education.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".