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Record W3087700005 · doi:10.1007/s10903-020-01079-2

What Role Can Trained Volunteers Add to Chronic Disease Care of Immigrants?

2020· article· en· W3087700005 on OpenAlexafffund
Ellen Rosenberg, Tamara E. Carver, Nina Mamishi, Gillian Bartlett

Bibliographic record

VenueJournal of Immigrant and Minority Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcGill UniversityCanadian Institute of Mining, Metallurgy and PetroleumMcGill University Health Centre
FundersFP7 HealthHealth CanadaMcGill University
KeywordsImmigrationMedicineQuality of life (healthcare)Public healthDiseaseHealth careFamily medicineChronic diseaseExploratory researchGerontologyNursingPsychology

Abstract

fetched live from OpenAlex

To help primary care teams improve patient-centered care, we elicited health and life goals of immigrants with a chronic disease. We conducted an exploratory study of the (1) acceptability of home visits by volunteers to collect health information and (2) content of health and life goals within a primary care program for immigrants with chronic disease. Pairs of trained community volunteers visited 23 patients in their homes and asked them to identify three life goals and three health goals. We conducted content analyses of written notes. Health goals were related to disease prevention and symptom control, family well-being, own quality of life, own or family members' work and/or financial situation. Life goals concerned family well-being, their own quality of life, work/financial situation and health. Given the limited time health professionals have with their patients, trained community volunteers could be important members of primary care teams caring for immigrants.

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)0.012
metaresearch head score (Gemma)0.042
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 score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.370
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 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
Published2020
Admission routes2
Has abstractyes

Explore more

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