MétaCan
Menu
Back to cohort
Record W2076914667 · doi:10.1080/13607860412331303793

Examining physician-patient-caregiver encounters: the case of Alzheimer's disease patients and family physicians in Israel

2004· article· en· W2076914667 on OpenAlexaff
Perla Werner, Amiram Gafni, Eliezer Kitai

Bibliographic record

VenueAging & Mental Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDementiaAffect (linguistics)DiseaseMedicineFamily medicinePsychologyPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

This study examines the characteristics of physician-patient-caregiver encounters in the presence of dementia and how sociodemographic and professional characteristics of family physicians, and severity of symptoms in patients with dementia affect these encounters. Phone interviews were conducted with 141 Israeli-Jewish family physicians (representing a 66% response rate), who were presented with one of two vignettes describing a 76-year old women with dementia. The two vignettes were identical, except that in the first it was stated that the woman sits quietly and cooperates during the examination whereas in the second she is agitated and uncooperative. Participants were asked to what extent they would ask questions to, inform and involve the patient and caregiver respectively when presented with one of the two vignettes. Findings showed that physicians would address the caregiver more than the patient (both with respect to questions, information and involvement). Moreover, it was found that physicians, who were older and had a higher number of years in the profession, would address the caregiver to a higher degree (compared to the patient) than younger and less experienced physicians. Findings provide direction for understanding medical encounters in the presence of dementia. Theoretical implications for dementia care, for medical encounters, and practical implications are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.214
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.385
Teacher spread0.276 · 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 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

Citations26
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueAging & Mental HealthSame topicPatient-Provider Communication in HealthcareFrench-language works237,207