Preparing nurses and nursing students to care for older adults in lower and middle-income countries: A scoping review
Bibliographic record
Abstract
BACKGROUND: The aim of this review was to examine the extent and nature of published literature on nurses' and nursing students' preparedness to care for older adults in lower and middle-income countries. METHOD: A scoping review with 27 articles, guided by the Arksey and O'Malley (2005) framework was conducted. The following electronic databases were searched: MEDLINE, CINAHL, ERIC and SCOPUS. Grey literature using Google Scholar and ProQuest were also searched. Key search terms and subject headings such as "nursing students", "nurses", "stakeholders", "ministry of health", "readiness", "knowledge", "attitudes", "elder care", "seniors care" and "older adults" were used to identify relevant articles. Studies that reported on gerontological nursing, nursing students' and nurses' knowledge and attitudes towards older adults, and stakeholders/employers' views about nursing students' knowledge and attitudes towards older adults were included. RESULTS: Nurses reported having moderate to insufficient knowledge in the care of older adults in the majority of the studies. Nursing students reported insufficient knowledge in the care of older adults. However, both nurses and nursing students reported positive attitudes towards the care of older adults but held misconceptions about caring for older adults. Nursing students also reported a low preference for working with older adults. DISCUSSION AND IMPLICATIONS: The inadequate preparation of nurses and nursing students in the care of older adults means there are opportunities to enhance knowledge about health and nursing care in lower and middle-income countries and build on the positive attitudes towards older adults.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".