Empathy in family medicine postgraduate education: A mixed studies systematic review
Bibliographic record
Abstract
Purpose Empathy is an important construct in patient-physician relationships, particularly critical in family physicians’ daily practice. We aimed to understand how empathy has been conceived and integrated into family medicine postgraduate training.Materials and Methods Medline, PsyINFO, and Embase were searched in this systematic mixed studies systematic review. Two independent reviewers screened abstracts and full texts. Disagreements were solved through research team consensus-based discussion. Included studies were synthesized thematically.Results A total of 18 studies were included. Four themes were identified. (1) Empathy definition. Included studies stressed the cognitive component of empathy, paired either with a behavioural or an affective response. (2) Empathy modifiers. Starting residency right after medical school, having a role model, having high empathy levels before residency, having children, being married, and being exposed to patient involvement in education were found to have a positive impact on empathy. (3) Empathy-burnout relationship. Whereas greater burnout was related to lower empathy levels, excess empathy seems to favour burnout through ‘compassion fatigue.’ (4) Educational programs for empathy development. Five programs were identified: a communication workshop, a patient-led program, a mindfulness program, a family-oriented intervention, and an arts-based program.Conclusions Studies mostly measured the cognitive component of empathy. The moral component of empathy was underrepresented in the conceptualization of empathy and the development of educational interventions. Conflicting evidence exists regarding the decline of empathy levels during the family medicine residency. Longitudinal designs should be privileged when exploring the evolution of empathy levels across the continuum of medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".