Burnout and empathy in primary care: three hypotheses
Bibliographic record
Abstract
In the last 10 years, a growing literature about empathy in primary care indicates that, for both patient and clinician, empathy helps to improve many aspects of health care practice and patient satisfaction. Wensing et al 1 indicated that humaneness, exploring the needs of patients, and good communication skills are some of the high priorities that patients expect from their GP. Cape et al 2 showed that doctor empathy coded by external observers was positively correlated with listening interactions and patient involvement. Vedsted and Heje3 found that patients tended to recommend their GP to others if they judged them as ‘empathic’. Mercer et al 4 showed a positive relationship both between GP empathy and patient enablement at the consultation, and between patient enablement and changes in the main symptom and well-being evaluated 3 months after the consultation. Finally, Verheul et al 5 investigated the impact of a warm and empathic consultation compared to a cold and formal consultation on the certainty of the outcome. The greatest beneficial effects of empathic communication on stress reduction and expectancies were observed when it was combined with a positive expectations style. To preserve effective empathy in care, the health provider must be in a positive frame of mind and not affected by stress or burnout. Burnout occurs when individuals feel emotionally overwhelmed by the …
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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".