Building Resilience: Psychological Approaches to Prevent Burnout in Health Professionals
Bibliographic record
Abstract
This study aims to explore and identify effective psychological approaches and interventions that can foster resilience and prevent burnout among health professionals. It seeks to understand how individual and organizational strategies can be integrated to support healthcare workers' mental well-being. The article employs a narrative review methodology, synthesizing existing research findings on resilience-building and burnout prevention strategies within healthcare settings. It examines both individual-level interventions, such as emotional intelligence training and stress management techniques, and organizational-level initiatives, including work environment improvements and policy changes. The review highlights that a combination of individual and organizational interventions is crucial for building resilience among health professionals. Key findings suggest that strategies focusing on enhancing emotional intelligence, promoting work-life balance, and creating a supportive work environment are effective in mitigating burnout. Furthermore, the importance of adaptive coping mechanisms and social support systems is emphasized. Building resilience in healthcare professionals is a multifaceted endeavor that requires both individual efforts and organizational support. The article concludes that implementing comprehensive, evidence-based interventions can significantly prevent burnout, ultimately leading to better healthcare outcomes and improved patient care. Future research should aim to address gaps in the current literature, particularly in assessing the long-term effectiveness of these interventions across diverse healthcare contexts.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".