Factors affecting undergraduate nurse educators' knowledge, skills or attitudes about older persons and their care: An integrative review
Bibliographic record
Abstract
BACKGROUND: Nurses are increasingly expected to provide care for older persons; however, there are too few nurse educators with expertise in older person care to ensure students graduate with the requisite competencies. METHODS: An integrative review, using Whittemore and Knafl's framework, was undertaken to identify and synthesise evidence about factors affecting nurse educators' knowledge, skills or attitudes about older persons and their care. RESULTS: Forty-four articles met the inclusion criteria. All but three papers originated in the USA. Content analysis yielded three central themes: external-level factors, employer-level factors and individual-level factors. Findings demonstrated that external funding from philanthropic organisations and government agencies supported many of the national, regional and site-specific initiatives, which were, in many cases, underpinned by professional regulatory frameworks. Negative attitudes of administrators and reduced budgets of educational institutions impeded the availability of such initiatives. Negative attitudes of individual educators towards older person care and the specialty of gerontology constrained their pursuit of such learning, as did their lack of awareness of current gerontology resources. CONCLUSIONS: The lack of educators with gerontology knowledge, skills and requisite attitudes requires a focused effort from external and professional bodies, and from educational institutions to ensure the resources are available to enhance educator expertise in gerontology. Rigorous study addressing the factors influencing educators' knowledge, skills or attitudes towards older persons and their care is required. IMPLICATIONS FOR PRACTICE: Addressing the lack of nurse educator expertise in gerontology could help to ensure new nurses have the required competencies to provide quality older person care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".