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Record W4416729700 · doi:10.63564/jnep.v15n11p52

Clinical training opportunities for nursing students in the nursing home

2025· article· W4416729700 on OpenAlexvenueno aff
Jacqueline Dunbar‐Jacob, Nancy Hodgson, Erin Kitt‐Lewis, Desiree Fleck, Donna M. Fick

Bibliographic record

VenueJournal of Nursing Education and Practice · 2025
Typearticle
Language
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersHillman FoundationJewish Healthcare FoundationHartford Foundation for Public Giving
KeywordsTeam nursingNurse educationNursing homesPrimary nursingGerontological nursingNursing researchHealth care

Abstract

fetched live from OpenAlex

The increasing proportion of older adults and the related increase in persons with chronic disease suggests increased attention to the training needs of nurses in care of the older adult. Research has shown that nurses are less knowledgeable about aging than other health care professionals. We propose that the nursing home offers an important training site for geriatric nursing and for care delivery in the sphere of supportive and rehabilitative care. Educational experiences offered through the Teaching Nursing Home Collaborative, an initiative to partner nursing homes and academic programs, funded by a collaboration of nonprofit foundations, explored the opportunities and factors that support or thwart successful clinical teaching. Factors included the nursing home environment, the faculty, and the interaction of faculty and nursing home staff. This manuscript outlines those findings along with a literature confirming the recommendations. In addition, we propose a collaborative model for success in an academic-service partnership.

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)0.006
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 score0.051
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0510.012

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 head0.559
GPT teacher head0.655
Teacher spread0.096 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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