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Record W2792224087 · doi:10.3389/fmed.2018.00048

A Volunteer Program to Connect Primary Care and the Home to Support the Health of Older Adults: A Community Case Study

2018· article· en· W2792224087 on OpenAlexafffund
Doug Oliver, Lisa Dolovich, Larkin Lamarche, Jessica Gaber, Ernie Avilla, Mehreen Bhamani, David Price

Bibliographic record

VenueFrontiers in Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcMaster University
FundersHealth CanadaGovernment of OntarioOntario Ministry of Health and Long-Term CareMcMaster University
KeywordsVolunteerPrimary careGerontologyPsychologyNursingMedicineFamily medicine

Abstract

fetched live from OpenAlex

Primary care providers are critical in providing and optimizing health care to an aging population. This paper describes the volunteer component of a program (Health TAPESTRY) which aims to encourage the delivery of effective primary health care in novel and proactive ways. As part of the program, volunteers visited older adults in their homes and entered information regarding health risks, needs, and goals into an electronic application on a tablet computer. A total of 657 home visits were conducted by 98 volunteers, with 22.45% of volunteers completing at least 20 home visits over the course of the program. Information was summarized in a report and electronically sent to the health care team via clients' electronic medical records. The report was reviewed by the interprofessional team who then plan ongoing care. Volunteer recruitment, screening, training, retention, and roles are described. This paper highlights the potential role of a volunteer in a unique connection between primary care providers and older adult patients in their homes.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.396
Teacher spread0.368 · 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 designCase report
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

Citations19
Published2018
Admission routes2
Has abstractyes

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