Burnout in oral health students: A scoping review
Bibliographic record
Abstract
BACKGROUND: Student burnout can be defined as the negative reactions that occur because of prolonged academic stress, which can result in emotional exhaustion/exhaustion (EE/EX), depersonalization/cynicism, and diminished personal accomplishment/reduced academic efficacy (DPA/RAE). OBJECTIVE: The purpose of this scoping review is to determine if burnout is prevalent in oral health students (OHS); identify the factors that are shown to be predicators of burnout in OHS; determine the preventive and coping strategies OHS used to mitigate the effects of burnout; and identify gaps in the literature on burnout in OHS. METHODS: A systematic search was completed using the following databases: PubMed, CINAHL, EBSCO, and ERIC. The returns were screened by all members of the team using inclusion and exclusion criteria, and the studies that met the criteria were appraised. RESULTS AND DISCUSSION: Eighteen studies assessed burnout in OHS, 15 studied dental students, 2 studied dental hygiene students, and 1 studied both. Findings concerning the prevalence of burnout varied greatly across the literature with anywhere between 7% and 70.4% of OHS reporting suffering from burnout syndrome. The most prevalent scale of burnout in OHS was EE/EX with 10%-66.2% of OHS reporting high levels. Stressors for OHS were years of study, clinical components, and demanding academic courses. Early identification and interventions are keys to prevent the negative consequences of burnout. CONCLUSION: Burnout in OHS can affect their mental health, empathy toward patients, and professional conduct. Therefore, educating students and faculty on the signs and symptoms of burnout is key in preventing detrimental effects that may inhibit their academic success.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".