Burnout and its subsequent impact on quality of care provided by nursing occupations in the long-term care sector: a scoping review protocol
Bibliographic record
Abstract
Introduction: Individuals who work in the nursing sector play a crucial role in the healthcare system, yet those working in long-term care (LTC) facilities often face challenges and are at risk of burnout due to occupational factors such as workload. This burnout can affect their ability to provide optimal care to their residents. This scoping review protocol aims to understand the impact of burnout on the capacity to deliver high-quality care among nursing-related occupations in LTC settings. Methods and analysis: A search will be conducted across Embase, CINAHL, Medline and APA PsycINFO. Studies will be included if they examine nursing professionals employed in the LTC sector, assess their experiences of burnout and explore how this burnout affects the quality of care they provide. No restrictions on publication year will be applied, and all study designs except knowledge syntheses will be considered. Eligible studies will be published in English or available in English translation. The study screening and data extraction will be completed by two independent reviewers. The included studies will undergo quality assessment using the Agency for Healthcare Research and Quality tool and the Newcastle-Ottawa Scale and the Critical Appraisal Skills Programme. A narrative synthesis approach will be used to report the findings. Ethics and dissemination: Ethics approval and consent to participate are not applicable. A scoping review will be prepared by a research team at the University of Toronto following the protocol procedure. Subsequently, this manuscript will be prepared and submitted to a peer-reviewed journal to disseminate study findings to the research community and contribute to the growing field of nursing well-being research.
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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 | 0.119 | 0.096 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.013 |
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".