Registered nurses’ reflections on their educational preparation to work with older people
Bibliographic record
Abstract
BACKGROUND: Negative perceptions about working with older people within nursing contribute to the deficit of educators with expertise to teach student nurses, and nurses graduating ill-equipped to work with the ageing population. The perceptions of nurses who have recently graduated from a nursing programme can provide insights into what they wished they knew about working with older people before they graduated. METHODS: A qualitative descriptive study design examined recently graduated registered nurses' reflections on their education preparation to work with older people. Content and thematic analysis was used to develop the themes of first impressions and preparation to work with older people. RESULTS: Key findings were that nurses did not recognise the importance of learning about older people until they had graduated. Only then did they realise that the ageing population was so complex and prevalent. They perceived a lack of education particularly related to working with older people with dementia and their behaviours, as well as learning how to communicate to an older population. Participants perceived that as students, it was up to them to fit in learning about working with older people without the support of faculty. CONCLUSIONS: Faculty need to be supported in learning how to best incorporate content about older people into their curriculum. This could include the development of learning activities that dispel negative stereotypes about ageing and facilitates interest in older people, as this is the population, students are most likely to work with when they graduate. IMPLICATIONS FOR PRACTICE: Nurses in practice may require education on working with people with dementia as it is a deficit in nursing programmes.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".