Outcomes of MBSR or MBSR-based interventions in health care providers: A systematic review with a focus on empathy and emotional competencies
Bibliographic record
Abstract
BACKGROUND: Emotional competencies are extremely important for healthcare providers exposed to patients' suffering. The effect of mindfulness-based stress reduction (MBSR) has been studied in this population. However, it is unclear whether capacities identified as core for care are modified favourably by this intervention. OBJECTIVES: (1) To identify outcomes in studies on the effect of MBSR in healthcare providers. (2) To evaluate the impact of MBSR on these outcomes. (3) To assess current knowledge on whether capacities central to care are positively impacted by MBSR: empathy, identification of one's own emotions, identification of other's emotions and emotional acceptance. METHODS: We performed a systematic review on interventional studies published up to 2015 evaluating the effect of MBSR in healthcare professionals. A subset of studies including empathy and emotional competencies was assessed for bias following current methodological standards. RESULTS: Thirty nine studies were identified. 14/39 studies measured empathy or some form of emotional competence in healthcare providers. Evidence regarding the effects of MBSR in professionals suggests this intervention is associated with improvements in burnout, stress, anxiety and depression. Improvements in empathy are also suggested but no clear evidence is currently available on emotional competencies. CONCLUSIONS: High quality evidence is available on the effect of MBSR on professionals' mental health. However, while some emotional competencies have been identified as being of major importance for high quality care, they are still scarcely studied. Studying these outcomes is important, as it may help explain how mindfulness contributes to professionals' mental health and thus help develop targeted interventions.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".