How nurses restore and maintain mobility in hospitalised older people: An integrative literature review
Bibliographic record
Abstract
AIM: The aim of this integrative review of the literature was to evaluate and summarise current research about how nurses maintain and improve hospitalised older peoples' mobility levels. BACKGROUND: Older persons make up the majority of healthcare recipients, and they are at risk to experience significant decline in their mobility once hospitalised. This can result in longer hospitalisations or nursing home admissions. Currently, it is not well understood how nurses maintain and restore mobility of hospitalised older persons. DESIGN: An integrative literature review using key concepts related to hospitalised older people, mobility and nursing care was conducted. Whittemore and Khalf's five-stage methodological framework for integrative reviews was utilised. METHODS: Two reviewers screened 1640 resources from four computerised databases published in English during 2000-2017. Reviewers used the Mixed Methods Appraisal Tool (MMAT) and CASP quality appraisal tools to assess the thirteen included articles. RESULTS: The findings of this review reveal that little is known about how frequently nurses are mobilising, that many nurses perceive mobilising older patients to be physiotherapy's responsibility and that education about mobilisation can improve nurses' willingness to mobilise people. CONCLUSION: By investing in education and training programmes targeted for nurses, nurses can feel empowered in their ability to mobilise patients and are encouraged to take ownership of their patient's functional needs. In order to facilitate mobility, adequate staffing levels are necessary for transferring and ambulation, mobility assistive devices such as walkers and canes and environments with adequate space to mobilise. More research is needed to better understand and overcome barriers that nurses face in mobilised older people in acute care. IMPLICATIONS FOR PRACTICE: The nursing team can work together to prioritise mobilisation to assist in restoring and maintaining the function of hospitalised older people. Educators could review their mobility programmes to increase graduate nurses' confidence and self-efficacy in mobility assessments and thus prepare graduate nurses for the realities of practice.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 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".