Improving the Psychosocial Work Environment to Prevent Sickness Absence and Turnover in Nurses: A Systematic Review
Bibliographic record
Abstract
In nurses, the high rates of sickness absence (SA) and turnover generate staggering costs. Their prevalence could be reduced by acting on the work‐related psychosocial factors (WRPFs) that can impact the mental health of nurses and lead to their absences and departures. However, there is a lack of consensus regarding the effectiveness of interventions that target WRPFs from a SA and turnover prevention perspective. This systematic review aims to identify interventions that target WRPFs to reduce SA and turnover in nurses, describe the methods used to evaluate their effectiveness, and report on their effectiveness at improving SA, turnover rates, and turnover intention. A systematic search was conducted using eight online databases and search engines (i.e., CINAHL, Embase, Catalogue ISST, Google Scholar, OSH Update, PsycINFO, PubMed, and Social SciSearch). Empirical studies that focus on an intervention that targets at least one WRPF and aims at reducing SA, turnover rates, or turnover intention among nurses were included. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument. Fourteen articles focusing on 13 interventions met the inclusion criteria. The interventions targeted individuals ( n = 4), groups ( n = 2), leaders ( n = 2), and organizations ( n = 5). The interventions aimed mainly at reducing workplace bullying and lateral violence and improving leadership. The research designs varied greatly across studies, and the results regarding the effects of the interventions on SA and turnover behaviors and intention were inconsistent. Innovative interventions targeting WRPFs need to be developed and implemented, and sophisticated methods to evaluate their effectiveness at reducing SA and turnover behaviors and intention should be employed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".