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Record W2582163955 · doi:10.12927/hcq.2017.25011

Primary Care Collaborative Memory Clinics: Building Capacity for Optimized Dementia Care

2017· article· en· W2582163955 on OpenAlexaff
Linda Lee, Loretta M. Hillier, Frank Molnar, Michael Borrie

Bibliographic record

VenueHealthcare Quarterly · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWestern UniversityParkwood InstituteLawson Health Research InstituteOttawa HospitalResearch Institute for Aging
Fundersnot available
KeywordsDementiaPrimary careNursingMedicinePsychologyFamily medicine

Abstract

fetched live from OpenAlex

Increasingly, primary care collaborative memory clinics (PCCMCs) are being established to build capacity for person-centred dementia care. This paper reflects on the significance of PCCMCs within the system of care for older adults, supported with data from ongoing evaluation studies. Results highlight timelier access to assessment with a high proportion of patients being managed in primary care within a person-centred approach to care. Enhancing primary care capacity for dementia care with interprofessional and collaborative care will strengthen the system's ability to respond to increasing demands for service and mitigate the growth of wait times to access geriatric specialist assessment.

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)0.032
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 score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0060.005
Open science0.0030.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.038
GPT teacher head0.385
Teacher spread0.347 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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