Interventions aimed at improving the nursing work environment: a systematic review
Bibliographic record
Abstract
BACKGROUND: Nursing work environments (NWEs) in Canada and other Western countries have increasingly received attention following years of restructuring and reported high workloads, high absenteeism, and shortages of nursing staff. Despite numerous efforts to improve NWEs, little is known about the effectiveness of interventions to improve NWEs. The aim of this study was to review systematically the scientific literature on implemented interventions aimed at improving the NWE and their effectiveness. METHODS: An online search of the databases CINAHL, Medline, Scopus, ABI, Academic Search Complete, HEALTHstar, ERIC, Psychinfo, and Embase, and a manual search of Emerald and Longwoods was conducted. (Quasi-) experimental studies with pre/post measures of interventions aimed at improving the NWE, study populations of nurses, and quantitative outcome measures of the nursing work environment were required for inclusion. Each study was assessed for methodological strength using a quality assessment and validity tool for intervention studies. A taxonomy of NWE characteristics was developed that would allow us to identify on which part of the NWE an intervention targeted for improvement, after which the effects of the interventions were examined. RESULTS: Over 9,000 titles and abstracts were screened. Eleven controlled intervention studies met the inclusion criteria, of which eight used a quasi-experimental design and three an experimental design. In total, nine different interventions were reported in the included studies. The most effective interventions at improving the NWE were: primary nursing (two studies), the educational toolbox (one study), the individualized care and clinical supervision (one study), and the violence prevention intervention (one study). CONCLUSIONS: Little is known about the effectiveness of interventions aimed at improving the NWE, and published studies on this topic show weaknesses in their design. To advance the field, we recommend that investigators use controlled studies with pre/post measures to evaluate interventions that are aimed at improving the NWE. Thereby, more evidence-based knowledge about the implementation of interventions will become available for healthcare leaders to use in rebuilding nursing work environments.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".