Interventions to prevent or reduce rationing or missed nursing care: A scoping review
Bibliographic record
Abstract
AIMS: To collate and synthesize published research on interventions developed and tested to prevent or reduce the rates of rationed or missed nursing care in healthcare institutions. BACKGROUND: Rationed and missed nursing care has been widely studied, including its predictors and associations with patient and nurse outcomes. DESIGN: Scoping review. DATA SOURCES: We searched for eligible studies, published between 1980-2019, in six electronic databases. REVIEW METHODS: Researchers independently screened the abstracts of the retrieved studies using the inclusion and exclusion criteria. The decision of whether or not to include any given study was consensus-based. RESULTS: The search yielded 1,815 records, of which 13 were included. Three studies reported structural interventions, namely increased nurse staffing and improved nursing teamwork, both resulted in significant reductions in the rates of rationed or missed nursing care. The remaining 10 studies reported on process interventions: four concerned reminders (via technology or designated persons) and seven described interventions to change or optimize the relevant care processes. All 10 process interventions contributed to significant reductions in the rates of missed nursing care. CONCLUSIONS: The results of the scoping review indicate that specific interventions can positively influence the performance of a selected nursing care activity, for example fall prevention. There is no evidence of a global reduction of rationed and missed nursing care through these interventions. IMPACT: Clinicians, managers and researchers can use the results for adapting and implementing interventions to reduce rationed and missed nursing care.
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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 | 0.046 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".