Assessing the Well-Being at Work of Nurses and Doctors in Hospitals: Protocol for a Scoping Review of Monitoring Instruments
Bibliographic record
Abstract
BACKGROUND: Well-being at work can be defined as "creating an environment to promote a state of contentment which allows an employee to flourish and achieve their full potential for the benefit of themselves and their organisation." In the health care context, well-being at work of nurses and doctors is important for good patient care. Moreover, it is strongly associated with individual- and organization-level consequences. Relevant literature presents models and concepts of physical, mental, and social well-being. This study uses the 6 elements of the job demands-resources (JD-R) model to interpret well-being at work (job demands, job resources, personal resources, leadership, well-being, and outcomes) as part of a Netherlands Federation of University Medical Hospitals program to find ways to improve and monitor health care professionals' well-being in Dutch hospitals. Many instruments exist to measure well-being at work in terms of population, setting, and other aspects. An overview of available and eligible instruments assessing and monitoring the well-being of nurses and doctors is currently missing. OBJECTIVE: We will perform a scoping review aiming to provide an overview of validated instruments assessing and monitoring the well-being of nurses and doctors at work. METHODS: We will perform a search of published literature in the following databases: Medline, Embase, and CINAHL. Studies will be eligible if they (1) assess well-being at work of nurses and doctors employed in hospitals; (2) describe an evaluation of an instrument or review an instrument; (3) measure well-being at work or aspects of well-being at work according to the elements of the JD-R model, and (4) were published in English from 2011 onwards. Title/abstract screening according to the eligibility criteria will be followed by full-text screening. Data extraction of included studies will be conducted by 3 reviewers independently. Reviewers will use standardized data extraction forms that include study characteristics, sample characteristics, measurement instrument details, and psychometric properties. The analysis will be descriptive. When synthesizing the data, a distinction will be made between comprehensive instruments and common instruments. RESULTS: This scoping review identifies instruments that have been developed and validated for monitoring the well-being of nurses and doctors at work. Studies were searched between September and December 2021 and screened between December 2021 and May 2022. A total of 739 studies were included. CONCLUSIONS: Timely screening of well-being at work may be beneficial for individual health care workers, the organization, and patients. There is often a substantial gap and mismatch between employer perceptions of well-being and well-being interventions. It is important to develop and implement suitable interventions adapted to the needs of nurses and doctors and their health or other problems. Well-being screening should be timely to gain insight into these needs and problems. Moreover, to determine the effectiveness of well-being interventions, measurement is mandatory. The results will be critical for organizations to select a monitoring instrument that best fits the needs of employees and organizations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/43692.
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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 | 0.089 | 0.082 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.015 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".