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Record W7117314308 · doi:10.1002/alz70858_103774

Impact of Embedded Memory Team in Primary Care

2025· article· en· W7117314308 on OpenAlexaff
K. Jennifer Ingram, Geneviève Arsenault‐Lapierre, Safea Altef

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityTrent University
Fundersnot available
KeywordsDementiaPrimary careProcess (computing)Memory problemsMEDLINEHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: The Primary Care - Embedded Memory Service (PC-EMS) implements a dementia diagnostic framework, leverages existing community and Family Health Team (FHT) personnel to create a Memory Assessment Team in Primary Care (PC) sites. This enables all PC sites to have the best practice tools necessary to diagnose dementia with consistency and all patients to be managed by their own family physician. These changes are necessitated by increasing patient volumes with memory concerns, delays in diagnosis, declining numbers of PC physicians, and long wait times for specialists. The aim is to describe the pilot implementation and evaluate the outcomes of the PC-EMS. METHODOLOGY: The PC-EMS model trains existing nursing staff and Alzheimer Society assessors to form a specialized dementia intake assessment team. A PC specific Dementia Care Pathway, incorporated into the Electronic Medical Record (EMR), provides a coordinated diagnostic process. The evaluation consisted of (1) physicians' knowledge, attitudes and practice (KAP) toward dementia care (2) patient and care partners satisfaction through surveys distributed once during the implementation, and (3) dementia quality as measured by retrospective review of PC-EMS charts compared to pre-intervention control group charts. RESULTS: 12 physicians and Nurse Practitioners of 15 referring physicians, 32 patients, and 27 care partners completed the surveys. 49 patients' charts and 50 control charts were reviewed. Patient and care partners' satisfaction and physicians' KAP scores were high. More importantly, documentation of key quality indicators (IADLs, BADLs, caregiver experience, and cognitive testing), time to follow-up by the patient's PC Physician, and use of specialist resources improved. The model addresses increasing patient volumes, ensures care partners' involvement, and improves the quality and consistency of charting. CONCLUSION: The pilot evaluation of the PC-EMS at the Medical Centre demonstrates an innovative program for dementia diagnosis within PC settings, which can be easily replicated and expanded to address future requirements for prompt person-centred dementia diagnosis. The results inform policy development and system change to ensure care partners and persons with dementia are jointly involved with their familiar PC physician in the process of establishing a diagnosis of dementia and initiating care.

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)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.336
Teacher spread0.320 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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