Evaluating the Effect of Supported Systematic Work Environment Management During the COVID-19 Pandemic: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The work environment is a complex phenomenon in which many factors interact. Scientific research indicates a relation between the work environment and employee health, staff turnover, patient satisfaction, and patient safety. There is a great need for knowledge on how to conduct work environment interventions and practical work environment management to maximize benefits to the employees. OBJECTIVE: The aim of this study is to explore how Occupational Health Service (OHS) support will affect the work environment, sick leave, staff turnover, patient satisfaction, and patient safety during and following the COVID-19 pandemic in a medical ward setting. METHODS: A mixed methods evaluation of a concurrent work environment quality improvement project at the Department of Internal Medicine and Geriatrics in a local hospital in the south of Sweden will be performed. RESULTS: The mixed methods evaluation of the quality improvement project received funding from Futurum-Academy for Health and Care, Jönköping County Council and Region Jönköping County, and the study protocol was approved by the Swedish Ethical Review Authority. The work environment quality improvement project will continue between May 2020 and December 2021. CONCLUSIONS: The study might contribute to increased knowledge of how work environment interventions and practical work environment management can impact the work environment, and employee health, staff turnover, patient satisfaction, and patient safety. There is a need for knowledge in this area for OHS management to provide increased benefits to employees, employers, and society as a whole. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34152.
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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 | 0.087 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".