Burnout Syndrome in Dental Professionals: Causes, Consequences, and Coping Strategies
Bibliographic record
Abstract
This study aimed to explore the causes, consequences, and coping strategies associated with burnout syndrome among dental professionals. This qualitative research utilized a phenomenological approach to examine the lived experiences of burnout among dental professionals. Data were collected through semi-structured interviews with 36 participants, including dentists, dental hygienists, and dental assistants, from diverse countries. Participants were recruited through online announcements, and interviews were conducted via video calls. Theoretical saturation determined the sample size. Interviews were transcribed and analyzed using NVivo software through inductive thematic analysis, involving open, axial, and selective coding to identify key themes related to burnout. The results indicated that burnout in dental professionals is primarily caused by excessive workload and time pressures, emotional exhaustion from patient care, lack of autonomy, workplace conflicts, financial pressures, and work-life imbalance. The consequences of burnout included chronic fatigue, sleep disturbances, anxiety, depression, professional dissatisfaction, strained interpersonal relationships, and a decline in patient care quality. Participants adopted various coping strategies, including stress management techniques such as mindfulness and relaxation, social support from colleagues and family, professional development through continuing education, organizational changes like improved workload distribution, and psychological coping mechanisms. These findings align with previous research on burnout and stress management in healthcare professionals. Burnout syndrome is a significant occupational challenge for dental professionals, leading to adverse physical, mental, and professional outcomes. Addressing burnout requires systemic changes, including better workload management, enhanced mental health resources, and promoting work-life balance. Future research should investigate long-term interventions and organizational policies to mitigate burnout in dentistry.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".