Gerontological educational interventions for student nurses: a systematic review of qualitative findings
Bibliographic record
Abstract
OBJECTIVES: This systematic review of qualitative studies explored interventions to improve student nurses' knowledge, attitudes or willingness to work with older people. Student nurses are likely to encounter older people in all health and aged care settings, however, research demonstrates that few have career aspirations in gerontological nursing. METHODS: Qualitative systematic review method based on the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: Search of Medline, Embase, PsycINFO, EBSCOhost and Scopus yielded 1841 articles which were screened to include primary research about educational interventions to improve student nurses' knowledge, attitudes and/or willingness to work with older people. Data extraction was performed on the 14 included studies, and data were analysed using directed content analysis. The Mixed Methods Appraisal Tool (MMAT) was used the assess the quality of the studies. CONCLUSIONS: Educational interventions included theory or practice courses, or a combination of theory and practice. While most interventions changed nursing students' negative attitudes towards older people, few increased their willingness to work with them. Practice courses had the most significant impact on willingness to work with older people. Quality assessment revealed methodical limitations. More research is needed to better understand the elements of practice interventions that enhance student nurses' knowledge, attitudes, and willingness to work with older people, so that they can be replicated.
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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 | 0.056 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".