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Record W2316122338 · doi:10.1017/s146342361300011x

Who steers the ship? Rural family physicians’ views on collaborative care models for patients with dementia

2013· article· en· W2316122338 on OpenAlexafffund
Julie Kosteniuk, Debra Morgan, Anthea Innes, John Keady, Norma J. Stewart, Carl D’Arcy, Andrew Kirk

Bibliographic record

VenuePrimary Health Care Research & Development · 2013
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchSaskatchewan Health Research Foundation
KeywordsDementiaEconomic shortageNursingFamily caregiversHealth careExploratory researchRural areaMedicineQualitative researchRural healthSample (material)Collaborative CarePsychologyPrimary careFamily medicineDiseaseSociology

Abstract

fetched live from OpenAlex

Little is known about the views of rural family physicians (FPs) regarding collaborative care models for patients with dementia. The study aims were to explore FPs' views regarding this issue, their role in providing dementia care, and the implications of providing dementia care in a rural setting. This study employed an exploratory qualitative design with a sample of 15 FPs. All rural FPs indicated acceptance of collaborative models. The main disadvantages of practicing rural were accessing urban-based health care and related services and a shortage of local health care resources. The primary benefit of practicing rural was FPs' social proximity to patients, families, and some health care workers. Rural FPs provided care for patients with dementia that took into account the emotional and practical needs of caregivers and families. FPs described positive and negative implications of rural dementia care, and all were receptive to models of care that included other health care professionals.

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)0.011
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.005
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
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 head0.036
GPT teacher head0.360
Teacher spread0.324 · 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 designQualitative
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

Citations19
Published2013
Admission routes2
Has abstractyes

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