MétaCan
Menu
Back to cohort
Record W4403989464 · doi:10.1515/ijnes-2023-0110

Educational interventions to improve student nurses’ knowledge, attitudes, or willingness to work with older people: a systematic review of quantitative findings

2024· review· en· W4403989464 on OpenAlexaff
Xingjuan Tao, Margaret MacAndrew, Sherry Dahlke, Jeffrey I. Butler, Jo‐Anne Rayner, Deirdre Fetherstonhaugh, Christina Parker

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2024
Typereview
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychological interventionBachelorInclusion (mineral)NursingSystematic reviewPsychologyIntervention (counseling)Medical educationGerontological nursingCritical appraisalMedicineMEDLINEAlternative medicineSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this systematic literature review of quantitative findings was to examine the effectiveness of educational interventions to improve gerontological knowledge, attitudes, and willingness to work with older people in baccalaureate nursing students. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic literature search was conducted in five databases. Quality assessment was conducted using the Mixed-Methods Appraisal Tool. Based on inclusion and exclusion criteria, 41 papers were included in the review. The overall quality of studies included was moderate. The interventions were classified as education content, simulation or immersion experiences, clinical placement, or a combination of these pedagogical approaches. Majority of studies demonstrated improvement in knowledge and attitudes but there was a limited change in willingness to work with older people. There is insufficient evidence to make recommendations for the most effective educational intervention for enhancing bachelor of nursing students' willingness to engage in gerontological care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.183
GPT teacher head0.607
Teacher spread0.424 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Nursing Education ScholarshipSame topicAging and Gerontology ResearchFrench-language works237,207