Facilitating intergenerational learning between older people and student nurses: An integrative review
Bibliographic record
Abstract
AIM: To examine the literature on intergenerational learning between older people and student nurses. BACKGROUND: Intergenerational activities offer opportunities for intergenerational learning and help reduce ageism. There are several older person/school children intergenerational learning initiatives. However, there is less known about how intergenerational learning occurs in nurse education programmes outside of service provision. METHODS: Whittemore and Knafl's (2005) integrative review framework was used to guide the review process. Population, intervention, context and outcome (PICO) was used to develop the review question, search strategy and inclusion/exclusion criteria. Database (CINAHL, Cochrane library, Medline, PubMed, Scopus and PsychInfo) searches and hand searching occurred from 2012 to 2023. Screening, appraisal and data extraction was undertaken according to Prisma guidelines. RESULTS: Nine papers were included (North American (n = 5), Canadian (n = 1) Chinese (n = 2), Taiwanese (n = 1)). Mixed methods designs were included. Four themes were identified: 1) Seeing beyond first glance; 2) Connecting and getting to know each other; 3) Learning together; and 4) Challenges for intergenerational learning. CONCLUSION: This review demonstrates the relevance of intergenerational learning in nurse education and highlights the importance of embedding initiatives which will promote and support mutual learning. Innovative intergenerational initiatives enable students to explore their underlying attitudes and views in a way that they may not be able to in the more traditional service and care giving learning situations.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".