Health care worker trust in the health care system, pre- and post-COVID-19 pandemic: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify the team, leadership, and organizational characteristics, behaviors, and traits that have created or reduced health care worker trust in the health care system pre- and post-COVID-19 pandemic. A secondary objective is to categorize the findings using the health care ecosystem as a descriptive framework (ie, teams, leadership, organizations, systems). INTRODUCTION: Trusting relationships and trustworthy organizational cultures promote employee well-being, satisfaction, and retention. High levels of trust are associated with ethical and just workplaces as well as high-functioning organizations with enhanced patient experiences. Emerging trust research in a post-pandemic climate correlates high health care worker trust with higher levels of patient trust, suggesting contributions to healthier workplaces and improved patient outcomes. ELIGIBILITY CRITERIA: The population is health care workers, the concept is trust, and the context is the health care system. We will consider all health care workers in any health care setting, in any country or position. All relevant published and unpublished studies will be considered, with no date or language limitations, including all primary studies, gray literature, and textual papers. METHODS: This review will follow the JBI methodology for scoping reviews, including the JBI approach to critical appraisal, study selection, data extraction, and data synthesis. Two reviewers will independently extract data from selected papers using a standardized tool modified for the review. Results will be presented using frequency tables, accompanied by a narrative summary.
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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 | 0.141 | 0.128 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.016 |
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".