Oral Healthcare Providers’ Well-being, Health and Experience (WHE) in Dental Care Settings: A Protocol for an Umbrella Review of Systematic Reviews and Meta-analyses
Bibliographic record
Abstract
Abstracts Objectives Over the decades, healthcare systems have faced significant pressures in supporting the well-being, health and experiences (WHE) of oral healthcare providers (OHCPs), while ensuring high quality of care. Emerging evidence reports physical, mental and social challenges among OHCPs, shaped by multilevel factors; however, a comprehensive synthesis of these issues is still needed. This umbrella review aims to summarize evidence on the barriers and enablers to OHCPs’ WHE in dental care settings. Methods The study will follow the Joanna Briggs Institute and Cochrane Handbook guidelines for umbrella reviews. The populations, intervention, context, outcomes and study design (PICOS) are defined as the following: (P) oral healthcare professionals, (I) definitions of WHE from authors, (C) any country or setting, (O) enablers and barriers on OHCPs’ WHE, (S) systematic reviews with and without meta-analysis. We will conduct the search strategy through six databases, including MEDLINE, Embase, CINAHL, Web of Science, PsychINFO, Cochrane Library, along with grey literature. Two independent reviewers will screen and select relevant reviews and extract data from systematic reviews and meta-analyses. We will assess methodological quality using the AMSTAR-2 and ROBIS tools. We will use the GROOVE tool for overlapping. We will synthesize barriers and enablers using the Consolidated Framework for Implementation Research. We will present the results in diagrammatic and tabular, along with a narrative summary. Results and conclusion The initial MEDLINE search identified 492 studies. The study findings will consolidate evidence on determinants impacting OHCPs’ WHE, providing insights for key stakeholders to foster safer and healthier dental care settings. Key Points What is already known on this topic Oral healthcare system is constantly changing and adapting to meet emerging social priorities and to deliver high quality and patient-centered care. As gatekeepers of the healthcare system, OHCPs’ WHE is essential to achieve the quintuple aim. Poor OHCP’s WHE is influenced by a range of factors, resulting in dental errors, adverse events, wastage in care and high costs. What this study adds This first umbrella review will summarize the multiple factors influencing positively and negatively OHCP’s WHE using the Consolidated Framework of Implementation Research, a robust and solid framework in implementation science. It will provide a better understanding of the challenges faced by OHCPs regarding their WHE and its impacts on a high quality of oral health care delivery. How did this study might affect research, practice and policy Understanding the importance of multiple levels (e.g., individual, contextual, and systemic) factors on OHCP’s WHE will provide valuable insights for decision makers, researchers, patients and guideline developers and OHCPs on OHCP’s WHE in developing successful implementation strategies to improve healthy work conditions to maximize their joy, and ultimately patient oral healthcare outcomes and experiences.
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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 | 0.166 | 0.176 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.063 | 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".