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Record W2151602352 · doi:10.1177/0733464813500585

Canadian Nursing Students and the Care of Older Patients

2013· article· en· W2151602352 on OpenAlexaffabout
Odette N. Gould, Suzanne Dupuis‐Blanchard, Anna MacLennan

Bibliographic record

VenueJournal of Applied Gerontology · 2013
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversité de MonctonMount Allison University
Fundersnot available
KeywordsNursingGerontological nursingFocus groupProfessionalizationNurse educationQualitative researchPsychologyPerceptionTeam nursingAged careMedicineSociology

Abstract

fetched live from OpenAlex

The aim of this research was to contribute to an understanding about the professionalization of gerontological nursing. The specific objective was to explore attitudes about older people among undergraduate nursing students. Three focus groups were carried out with 3rd-year nursing students in a generalist program in a small Canadian city and discussions focused on experiences and attitudes surrounding the care of older patients. A qualitative descriptive approach was used to analyze the verbatim transcripts. Results indicated that students had positive reactions to caring for older patients, at least when dementia is not present, but they received a strong message from their mentors that this type of nursing is neither prestigious nor valued. Discussions surrounding the care of older adults highlighted students' perceptions of conflicts between the art and science of nursing, and their concerns regarding the divisions of tasks between nursing students, registered nurses, and licensed practical nurses.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.004
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.020
GPT teacher head0.349
Teacher spread0.329 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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