Systematic review of evidence on the impact of nursing workload and staffing on establishing healthy work environments
Bibliographic record
Abstract
UNLABELLED: Background This systematic review set out to examine the impact, if any, of nursing workload and staffing on creating and maintaining healthy work environments. For the purposes of this review, the term 'healthy work environment' was defined as '. . . a practice setting that maximizes the health and well-being of nurses, quality patient outcomes and organizational performance'. This definition identifies nurse, patient and organisational outcomes as indicators of the establishment and maintenance of a healthy work environment. Objectives The review sought to determine the impact of: • Patient characteristics, nurse characteristics, system characteristics and system processes on workload, scheduling and concepts of productivity and utilisation • Workload, scheduling and concepts of productivity and utilisation on the quality of outcomes for clients, nurses and the system/organisation Search strategy The search strategy sought to find both published and unpublished studies and papers written in the English language. A three-step search strategy approach was used. An initial limited search of MEDLINE and CINAHL databases was undertaken to identify optimal search terms followed by an analysis of the text words contained in the title and abstract, and of the index terms used to describe the article. A second extensive search using all identified keywords and index terms was then undertaken. The third step consisted of a search of the reference lists of all identified reports and articles for additional studies. Selection criteria Types of studies: This review considered research papers that addressed the appropriateness and effectiveness of workload and staffing concepts in fostering a healthy work environment in healthcare. The types of papers to be considered included: meta-analysis, randomised controlled trials, quasi-randomised controlled trials, cohort studies, case-control studies, descriptive studies and correlational studies. TYPES OF PARTICIPANTS: The review considered all participants involved or affected by workload and staffing concepts within the nursing workforce in a healthcare environment, including staff and patients. System and policy issues were also considered. Types of interventions: All workload and staffing strategies that impact on the work environment, patient and nurse outcomes were considered in this review. Types of outcome measures: Outcomes of interest were categorised into four groups: nursing staff outcomes, patient outcomes, organisational outcomes and system outcomes. Data collection and analysis Following assessment of methodological quality, data were extracted using data extraction tools based on the work of the Cochrane Collaboration and the Centre for Reviews and Dissemination. Statistical pooling was not possible and findings were presented in narrative form. Results Of the 2162 papers identified in the search, 171 were selected for full paper retrieval and assessed independently by two reviewers for methodological quality. A total of 40 papers were included in the review: one systematic review; one cohort study; and 38 correlational descriptive studies. Results were summarised in narrative form. The evidence suggests strong correlations between patient characteristics and work environments; and workload and staffing and the quality of outcomes for clients, nurses and the system/organisation. This gave rise to a number of recommendations for practice and for further research, such as: • A greater proportion of regulated staffing (i.e. registered nurses, enrolled nurses, practical or vocational nurses) is associated with improved outcomes related to the Functional Independence Measure score, the Short Form Health Survey (SF-36) vitality score, patient satisfaction with nursing care, patient adverse events (including atelectasis, decubitus ulcers, falls, pneumonia, postsurgical and treatment infection and urinary tract infections) • An increase in the number of registered nurse hours available is associated with improved patient outcomes in relation to falls, pneumonia, pressure ulcers, urinary tract infection, length of stay and postoperative infection rates.
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 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 | 0.027 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".