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Record W2806846351 · doi:10.1186/s12875-018-0756-z

Health care provider experiences in primary care memory clinics: a phenomenological study

2018· article· en· W2806846351 on OpenAlexafffund
Linda Sheiban, Paul Stolee, Carrie McAiney, Véronique Boscart

Bibliographic record

VenueBMC Family Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsResearch Institute for AgingMcMaster UniversityUniversity of WaterlooConestoga College
FundersCanadian Institutes of Health Research
KeywordsMedicinePrimary carePrimary health careFamily medicineHealth careQualitative researchNursing

Abstract

fetched live from OpenAlex

BACKGROUND: There is a growing need for community-based services for persons with Alzheimer's disease and related dementias (ADRD). Memory clinic (MC) teams in primary care settings have been established to provide care to people with ADRD. To consider wider adoption of these MC teams, insight is needed into the experiences of practitioners working in these models. The purpose of the current study is to explore the experiences of health care providers (HCPs) who work in primary care Memory Clinic (MC) teams to provide care to persons with Alzheimer's disease and related dementias (ADRD). METHODS: This study utilized a phenomenological methodology to explore experiences of 12 HCPs in two primary care MCs. Semi-structured interviews were completed with each HCP. Interviews were recorded and transcribed verbatim. Colaizzi's steps for analyzing phenomenological data was utilized by the authors. RESULTS: Three themes emerged from the analysis to describe HCP experiences: supporting patients and family members during ADRD diagnosis and treatment, working in a team setting, and personal and professional rewards of caring for people with ADRD and their family members. CONCLUSIONS: Findings provide insight into current practices in primary care MCs and on the motivation of HCPs working with persons with ADRD.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.337
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

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

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 head0.074
GPT teacher head0.419
Teacher spread0.344 · 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.

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

Citations8
Published2018
Admission routes2
Has abstractyes

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