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Record W4385226494 · doi:10.2196/47822

Peer Support Self-Management Intervention for Individuals With Type 2 Diabetes in Rural Primary Care Settings: Protocol for a Mixed Methods Study

2023· article· en· W4385226494 on OpenAlexvenueno aff
Xuefeng Zhong, Shaohua Li, Meng Luo, Xinyu Ma, Edwin B. Fisher

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesAnhui University
KeywordsProtocol (science)Peer supportIntervention (counseling)Self-managementType 2 diabetesMedicinePsychologyPrimary careNursingFamily medicineDiabetes mellitusComputer scienceAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The increasing prevalence of diabetes is placing important demands on the Chinese health care system. Providing self-management programs to the fast-growing number of people with diabetes presents an urgent need in rural primary care settings in China. Peer support has demonstrated effectiveness in improving self-management for individuals with diabetes in urban communities in China. A priority then becomes developing and evaluating a peer support program in primary care settings in rural communities of China and determining whether it is feasible and acceptable. OBJECTIVE: The aims of this study are (1) to evaluate the feasibility and acceptability of a peer support approach to type 2 diabetes self-management in rural primary care settings; (2) to identify enabler and facilitator factors likely to influence the peer support implementation; (3) to provide primary data and evidence for developing a version of the program suitable for a randomized controlled trial in rural primary care settings. METHODS: Three townships will be sampled from 3 different counties of Anhui province as the study setting. Participants will be recruited based on these counties' local primary care health record system. The peer supporters will be recruited from among the participants. The peer support program will be led by peer supporters who have completed 12 hours of training. It will be guided by primary care providers. The program will include biweekly meetings over 3 months with varied peer support contacts between meetings to encourage the implementation of diabetes self-management. Mixed methods will be used for evaluation. Qualitative methods will be used to collect information from health care system professionals, individuals with diabetes, and peer supporters. Quantitative methods will be used to collect baseline data and data at the end of the 3-month intervention regarding psychosocial factors and self-management practices. RESULTS: The results will include (1) quantitative baseline data that will characterize type 2 diabetes self-management practices of individuals with diabetes; (2) qualitative data that will identify enablers of and barriers to self-management practices for individuals with type 2 diabetes in rural communities; (3) both qualitative and quantitative evaluation data, after the 3-month intervention, to demonstrate the feasibility and acceptability of the peer support approach for individuals with type 2 diabetes. CONCLUSIONS: Our findings will inform the design of a tailored intervention program to improve self-management among individuals with type 2 diabetes in rural primary care settings. If we find that the peer support approach is feasible and acceptable, we will develop a larger randomized controlled trial to evaluate effectiveness in multiple rural settings in the province. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47822.

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)0.042
metaresearch head score (Gemma)0.026
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 score0.056
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.026
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0030.003
Science and technology studies0.0060.003
Scholarly communication0.0040.003
Open science0.0050.003
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0560.007

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 head0.139
GPT teacher head0.565
Teacher spread0.426 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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