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Record W2141741138 · doi:10.1093/geront/43.2.259

Effectiveness of Continuing Education in Long-Term Care: A Literature Review

2003· review· en· W2141741138 on OpenAlexaff
Stephen Aylward, Paul Stolee, Nancy Keat, Van Johncox

Bibliographic record

VenueThe Gerontologist · 2003
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsThames Valley Children's CentreWestern UniversitySt Joseph's Health Care
Fundersnot available
KeywordsContinuing educationTerm (time)Grey literatureMedical educationSelection (genetic algorithm)Continuing careKnowledge managementPsychologyMEDLINEMedicineNursingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

PURPOSE: This review of the literature examines the effectiveness of continuing education programs in long-term care facilities. DESIGN AND METHODS: A comprehensive literature search was made for evaluation studies and included computerized bibliographic databases, manual searches of journals, the bibliographies of retrieved articles, and information from key informants. RESULTS: Forty-eight studies met our selection criteria. Rigorous research in this area has been limited. Because of the lack of follow-up evaluation, there is minimal evidence that knowledge gained from training programs is sustained in the long term. Most studies do not consider organizational and system factors when planning and implementing training initiatives. This may account for difficulties encountered in the sustained transfer of knowledge to practice. IMPLICATIONS: There is a need for further rigorous research on the effectiveness of continuing education in long-term care, with systematic attention to the role of organizational and system factors.

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)0.012
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0070.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.045
GPT teacher head0.461
Teacher spread0.415 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations170
Published2003
Admission routes1
Has abstractyes

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