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Record W4367322991 · doi:10.2196/46156

Peer-to–Patient-Aligned Care Team (Peer-to-PACT; P2P), a Peer-Led Home Visit Intervention Program for Targeting and Improving Long-term Care Services and Support for Veterans With High Needs and High Risk: Protocol for a Mixed Methods Feasibility Study

2023· article· en· W4367322991 on OpenAlexvenueno aff
Sandra Garcia‐Davis, Ana Palacio, Elizabeth Bast, Lauren Penney, Erin P. Finley, Bruce Kinosian, Orna Intrator, Stuti Dang

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersNIH Clinical CenterU.S. Department of Veterans Affairs
KeywordsVeterans AffairsPeer supportNursingIntervention (counseling)MedicineHealth careEmpowermentCoachingPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Keeping older veterans with high needs and high risk (HNHR) who are at risk of long-term institutional care safely in their homes for as long as possible is a Department of Veterans Affairs priority. Older veterans with HNHR face disproportionate barriers and disparities to engaging in their care, including accessing care and services. Veterans with HNHR often have poor ability to maintain health owing to complicated unmet health and social needs. The use of peer support specialists (peers) is a promising approach to improving patient engagement and addressing unmet needs. The Peer-to-Patient-Aligned Care Team (Peer-to-PACT; P2P) intervention is a multicomponential home visit intervention designed to support older veterans with HNHR to age in place. Participants receive a peer-led home visit to identify unmet needs and home safety risks aligned with the age-friendly health system model; care coordination, health care system navigation, and linking to needed services and resources in collaboration with their PACT; and patient empowerment and coaching using Department of Veterans Affairs whole health principles. OBJECTIVE: The primary aim of this study is to evaluate the preliminary effect of the P2P intervention on patient health care engagement. The second aim is to identify the number and types of needs and unmet needs as well as needs addressed using the P2P needs identification tool. The third aim is to evaluate the feasibility and acceptability of the P2P intervention delivered over 6 months. METHODS: We will use a quantitative-qualitative convergent mixed methods approach to evaluate the P2P intervention outcomes. For our primary outcome, we will conduct an independent, 2-tailed, 2-sample t test to compare the means of the 6-month pre-post differences in the number of outpatient PACT encounters between the intervention and matched comparison groups. Qualitative data analysis will follow a structured rapid approach using deductive coding as well as the Consolidated Framework for Implementation Research. RESULTS: Study enrollment began in July 2020 and was completed in March 2022. Our sample size consists of 114 veterans: 38 (33.3%) P2P intervention participants and 76 (66.7%) matched comparison group participants. Study findings are expected to be published in late 2023. CONCLUSIONS: Peers may help bridge the gap between PACT providers and veterans with HNHR by evaluating veterans' needs outside of the clinic, summarizing identified unmet needs, and developing team-based solutions in partnership with the PACT. The home visit component of the intervention provides eyes in the home and may be a promising and innovative tool to improve patient engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46156.

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)0.026
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.042
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.017
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0420.007

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 head0.086
GPT teacher head0.531
Teacher spread0.445 · 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 designNot applicable
Domainnot available
GenreProtocol

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
Published2023
Admission routes1
Has abstractyes

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