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Record W4361300666 · doi:10.1177/14713012231165568

A scoping review to inform the development of dementia care competencies

2023· review· en· W4361300666 on OpenAlexaffabout
Kelly Kay, Kateryna Metersky, Victoria Smye, Colleen McGrath, Karen Johnson, Arlene Astell, Winnie Sun, Emma Bartfay

Bibliographic record

VenueDementia · 2023
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsOntario Tech UniversityWestern UniversityUniversity Health NetworkToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsDementiaHealth careVariety (cybernetics)PsychologyCore competencyNursingSituatedMedical educationMedicineDiseaseBusinessComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Health professionals and care partners of persons living with dementia have expressed that learning needs related to dementia care are a priority. There are currently a variety of training programs available in Ontario (Canada) to address aspects of dementia care, but no commonly accepted description of the core knowledge, skills, and abilities, (i.e., competencies) that should underpin dementia-related training and education in the province. The aim of this study was to review current evidence to inform the later development of competency statements describing the knowledge, skills and actions required for dementia care among care providers ranging from laypersons to health professionals. We also sought to validate existing dementia care principles and align new concepts to provide a useful organizing framework for future competency development. We distinguished between micro-, meso- and macro-level concepts to clarify the competencies required by individuals situated in different locations across the healthcare system, linking competency development in dementia care to broader system transformation. This review precedes the co-development of a holistic competency framework to guide approaches to dementia care training in Ontario.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.137
GPT teacher head0.444
Teacher spread0.306 · 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 teacher head, not a consensus.

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

Citations9
Published2023
Admission routes2
Has abstractyes

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