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Record W1999740909 · doi:10.3928/01484834-20110429-03

Case Study of the Attitudes and Values of Nursing Students Toward Caring for Older Adults

2011· article· en· W1999740909 on OpenAlexaff
Courtney Evers, Jenny Ploeg, Sharon Kaasalainen

Bibliographic record

VenueJournal of Nursing Education · 2011
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGerontological nursingPsychologyIndependence (probability theory)Older peopleExploratory researchNursingGerontologyPopulationMedicine

Abstract

fetched live from OpenAlex

Given the aging population and the complex needs of older adults, there is considerable need for additional gerontological nurses. This pilot study explored fourth-year nursing students' attitudes and values toward caring for older adults and the influence of their experiences with older adults on these attitudes and values. Using Yin's exploratory case study design, 51 fourth-year nursing students constituted the single case. An initial quantitative survey placed students in three embedded units of analysis: neutral, pro-aged, and anti-aged bias toward older adults. Using purposeful sampling from each of the embedded units, 9 students were interviewed. Four main values (respect, caring, independence, and wisdom and experience) and five attitudes (enjoy older adults, see older adults as normal, feel sorry for older adults, are frustrated by older adults, and dislike gerontological nursing) were identified. The authors' findings have important implications for education and practice.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
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 head0.175
GPT teacher head0.505
Teacher spread0.330 · 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 designQualitative
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

Citations37
Published2011
Admission routes1
Has abstractyes

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