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Record W3110834897 · doi:10.31372/20200503.1087

Efficacy of a Culturally and Linguistically Competent Community Health Coach Intervention for Chinese with Hypertension

2020· article· en· W3110834897 on OpenAlexvenueno aff
Wen Li, Donna Lew, Linda Quach

Bibliographic record

VenueAsian/Pacific Island Nursing Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersAmerican Heart Association
KeywordsBlood pressureMedicineIntervention (counseling)Physical therapyDiastoleImmigrationInternal medicineNursing

Abstract

fetched live from OpenAlex

Purpose: To develop and pilot test the efficacy of a culturally and linguistically sensitive, community health coach (CHC)-based intervention in Chinese immigrants in improving blood pressure control and medication adherence. Design: This study was conducted in 2017 with a cross-sectional design (n = 23). A CHC intervention was implemented using one 25-minute group educational presentation plus one 10-minute question and answer session at baseline, followed by four, 10-minute bi-weekly group question-and-answer sessions. Findings: There was a significant reduction in both systolic and diastolic blood pressure from baseline to week 8: Systolic BP −17.33 (±11.32) (p < 0.005) and diastolic BP −9.58 (±6.57) (p < 0.005). The mean score for medica- tion adherence was 10.56 (±3.24) (possible range 3–15) at baseline and there was no significant change at week 8 (mean 10.89 ± 3.95) (p = 0.86). Conclusion: The CHC-based hypertension management program showed significant reductions in both systolic and diastolic blood pressures in Chinese immigrants. Since the proposed CHC-based hypertension management program is low cost and easy to establish, further investigation is recommended to generate more results for comparison. Practice Implications: There is potential for the CHC intervention to be implemented in clinical settings to help Chinese immigrants at large achieve optimal blood pressure control.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.318

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.309
Teacher spread0.283 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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