Nursing practice with hospitalised older people: Safety and harm
Bibliographic record
Abstract
BACKGROUND: Nursing teams work with hospitalised older people in institutions, which prioritise a biomedical model of care. This model does not fit the needs of older people because it emphasises efficacy and a narrow definition of patient safety, but does not prioritise functional needs. Nursing care is provided around the clock within the context of fiscal restraints as well as negative societal and nursing perspectives about ageing and old people. Yet, nursing perceptions of managing safety and potential harms to older patients within these hospital institutions are not well understood. METHODS: An integrative review was conducted to examine nursing perspectives of safety and harm related to hospitalised older people. RESULTS: The majority of included papers focused on restraint use. Findings reveal that nurses are using restraints and limiting mobility as strategies to manage their key priority of keeping older patients safe, reflecting a narrow conceptualisation of safety. Policy, administrative support and individual nurse characteristics influence restraint use. Safety policies that nurses interpret as preventing falls can encourage the use of restraints and limiting mobility, both of which result in functional losses to older people. CONCLUSIONS: This complex issue requires attention from clinical nurses, leaders, policy makers and researchers to shift the focus of care to preservation and restoration of function for older people in hospital as a safety priority. IMPLICATIONS FOR PRACTICE: Clinical leaders and nursing teams should engage in developing processes of care that incorporate maintaining and restoring older people's function.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".