Understanding compassion in family medicine: a qualitative study
Bibliographic record
Abstract
Background Patients and physicians have traditionally valued compassion; however, there is concern that physician compassion has declined with the increasing emphasis on science and technology in medicine. Although the literature on compassion is growing, very little is known about how family physicians experience compassion in their work. Aim To explore family physicians’ capacity for and experiences of compassion in practice. Design and setting This was a qualitative study designed using a phenomenological approach in rural and urban Ontario, Canada. Method In-depth interviews were audiotaped and transcribed verbatim, followed by independent and team coding. An iterative and interpretive analysis was conducted using immersion and crystallisation techniques. Purposive sampling recruited 22 participants (nine males and 13 females aged 26–64 years) that included family medicine residents from Western University ( n = 6), and family physicians practising <5 years ( n = 7) or >10 years ( n = 9) in Ontario, Canada. Results From the data, the authors derived the Compassion Trichotomy as a theoretical model to describe three interrelated areas that determine the evolution or devolution of compassion experienced by family physicians: motivation (core values), capacity (energy), and connection (relationship). Conclusion The Compassion Trichotomy highlights the importance and interdependence in physician compassion of motivation (personal reflection and values), capacity (awareness and regulation of energy, emotion, and cognition), and connection (sustained patient–physician relationship). This model may assist practising family physicians, educators, and researchers to explore how compassion development might enhance physician effectiveness and satisfaction.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".