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Record W2015308720 · doi:10.2202/1548-923x.1131

Helping Baccalaureate Nursing Students Care for the Wellbeing of Older Adults

2005· article· en· W2015308720 on OpenAlexaff
Ti King

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2005
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsLakehead University
Fundersnot available
KeywordsCONTESTNursingGerontological nursingBaccalaureate DegreeNurse educationPsychologyMedical educationPopulation ageingMedicinePopulationGerontologyHigher education

Abstract

fetched live from OpenAlex

Because the world's population is aging there is a dire need for gerontologically prepared registered nurses. Baccalaureate nursing students, therefore, need experiences with older adults. However, due to constraints on their practice, students might have insufficient opportunities to learn about how to contribute to older adults' wellbeing. Students provided with a combination of learning experiences may understand how to contest constraints on their practice and create conditions that facilitate the provision of age-responsive care for older adults. In this paper gerontological-nursing content, constraints on practice, and learning experiences to help students come to understand their contributions to older adults' wellbeing are identified. Potential benefits of these learning experiences for students, older adults, and the nursing profession are discussed.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.061
GPT teacher head0.494
Teacher spread0.433 · 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 designNot applicable
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

Citations5
Published2005
Admission routes1
Has abstractyes

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