Use of the Maslach Burnout Inventory Among Public Health Care Professionals: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Burnout syndrome is a chronic response to stressors in the workplace. It is characterized by emotional exhaustion and physical and mental burnout and may lead to high employee turnover, work absenteeism, and increased occupational accidents. Most studies use the Maslach Burnout Inventory (MBI) to identify burnout and implement preventive actions and treatments. OBJECTIVE: This study presents a scoping review protocol to identify and map studies that used MBI to assess burnout syndrome in health care professionals working in public health services. METHODS: This scoping review protocol follows the Joanna Briggs Institute reviewers' manual, and this protocol consists of 6 stages: identifying the research question, identifying relevant studies, study selection, data extraction and coding, analysis and interpretation of results, and consultation with stakeholders. We will conduct searches in Embase, LILACS, PubMed/MEDLINE, PsycINFO, Scopus, Web of Science databases, and gray literature. The main research question is as follows: how is MBI used to identify burnout syndrome in health care professionals working in public health services? Inclusion criteria will comprise qualitative and quantitative studies using MBI to identify burnout syndrome in health care professionals working in public health services and no restrictions in language and publication dates. Data will be extracted using a spreadsheet adapted from the Joanna Briggs Institute model. Quantitative and qualitative data will be analyzed using descriptive statistics and thematic analysis, respectively. The consultation with stakeholders will be essential for increasing the knowledge about MBI, identifying new evidence, and developing future strategies to guide public policies preventing burnout syndrome in health care professionals working in public services. RESULTS: This protocol will guide a scoping review to identify and map studies that used MBI to identify burnout syndrome in health care professionals working in public health services. The results of this review may be useful to public health care professionals, managers, policymakers, and the general population because these findings will help understand the validated, translated, and adapted versions of MBI and domains, number of items, Likert scales, and cutoff points or the latent profile analysis most used in the literature. Furthermore, possible research gaps may be identified to guide future studies. All information regarding the stages of the scoping review favor its transparency and allow it to be methodologically replicated according to the principles of open science, thereby reducing the risk of bias and data duplication. CONCLUSIONS: This study may reveal the multiplicity of scales described in the literature and the different forms of assessing burnout syndrome in health care professionals. This study may help to standardize the assessment of burnout syndrome in health care professionals working in public health services and contribute to the discussion and knowledge dissemination about burnout syndrome and mental health in this population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42338.
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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 | 0.117 | 0.126 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.015 |
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".