Interventions to improve resilience in physicians who have completed training: A systematic review
Bibliographic record
Abstract
BACKGROUND: Resilience is a contextual phenomenon where a complex and dynamic interplay exists between individual, environmental, and socio-cultural factors. With growing interest in enhancing resilience in physicians, given their high risk for experiencing prolonged or intense stress, effective strategies are necessary to improve resilience and reduce negative outcomes including burnout. The objective of this review was to identify effective interventions to improve resilience in physicians who have completed training, working in any setting. METHODS AND FINDINGS: We included randomized controlled trials (RCT), and observational studies (including pilot studies) published in English, French, and Spanish that included an intervention to improve resilience in physicians who have completed training. We included studies that implemented interventions to reduce burnout, anxiety, and depression or to improve empathy to ultimately enhance resilience, rather than studies designed solely to reduce stress or trauma-induced stress. We performed a systematic search of Medline, EMBASE, PsychInfo, CINAHL and Cochrane Library with no publication year limit. The last search was conducted on March 29, 2017. We used random effect models to calculate pooled standardized mean differences. Resilience was the primary outcome measure using validated resilience scores. Secondary outcome measures included proxy measures of resilience such as burnout, empathy, anxiety and depression. Our search strategy identified 7,579 records;74 met the criteria for full-text review. Seventeen studies were included in the final review published between 1998 and 2016 of which 9 (4 RCT, 5 observational) had physician data extractable. Interventions varied greatly regarding their approach, duration, and follow-up. Two RCTs measured resilience using validated scales; both found a significant improvement. No meta-analysis for resilience was conducted due to the presence of high clinical and methodological heterogeneity. CONCLUSIONS: Our systematic review demonstrates that there is weak evidence to support one intervention over another to improve resilience in physicians who have completed training. The quality of evidence for the outcomes ranged from very low to low. There is a need for a consensus on the definition of resilience and how it is measured. Longer follow-up is required to ensure any intervention effects are sustained over time.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| 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) | 0.005 | 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".