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Record W2154925996 · doi:10.1353/cja.2007.0016

Long-Term Care Planning Study: Strengths and Learning Needs of Nursing Staff

2006· article· en· W2154925996 on OpenAlexaffabout
Kathleen Cruttenden

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNursingPsychologyNurse educationMedicine

Abstract

fetched live from OpenAlex

This planning study was designed and conducted in a predominantly rural Canadian province to examine the strengths and learning needs of four categories of nursing staff practising in New Brunswick nursing homes. Participants included directors of care, registered nurses, licensed practical nurses, and resident attendants. The nursing homes ranged in size from 38 to 196 beds and were located throughout the province. In health and planning studies, ethnography conveys a coherent statement of peoples' local knowledge as culture-sharing groups (Muecke, 1994). The study derived information from the Nursing Home Act, reports, the literature, key informants, and direct observations of and interviews with participants. Leadership strengths defined the roles for categories of staff and supported the capacity of each category to identify their learning needs. In conclusion, nurses practising in nursing homes can and must take an active role in decision making for their learning.

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)0.005
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 score0.398
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.313
Teacher spread0.296 · 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

Citations8
Published2006
Admission routes2
Has abstractyes

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Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicGeriatric Care and Nursing HomesFrench-language works237,207