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Record W2978037290 · doi:10.1515/ijnes-2019-0051

Student Nurses’ Perceptions About Older People

2019· article· en· W2978037290 on OpenAlexafffundabout
Sherry Dahlke, Sandra Davidson, Uirá Duarte Wisnesky, Maya R. Kalogirou, Vincent Salyers, Cheryl Pollard, Mary Fox, Kathleen F. Hunter, Jennifer Baumbusch

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2019
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsYork UniversityUniversity of British ColumbiaMacEwan UniversityUniversity of CalgaryUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerceptionNursingOlder peopleLicensureScale (ratio)Health carePsychologyPopulation ageingGerontological nursingSituatedPopulationMedical educationMedicineGerontology

Abstract

fetched live from OpenAlex

Educating nursing students about the ageing population is situated within negative societal, heath care and nursing perceptions. A cross-sectional design using Burbank's perceptions towards older people scale was used to survey students in a pre-licensure nursing program in western Canada. Findings revealed that students' perceptions about older people were lower in the third year of the nursing program and after four clinical experiences. We suggest that students' first experiences in long-term care settings, in which they learn to provide basic care to older people, be balanced with experiences of older people in a variety of settings. Such experiences would allow students to develop the knowledge and skill needed to work with an ageing population with complex healthcare needs. More research is needed to better understand students' experiences and perceptions about where in the program more learning strategies about how to best work with older people would be helpful.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.054
GPT teacher head0.508
Teacher spread0.454 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2019
Admission routes3
Has abstractyes

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