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Record W4405619227 · doi:10.1177/23743735241309474

The Role of Respect and Collaborative Decision Making on Diabetes Care Factors Among Nonpregnant Women of Reproductive Age With Diabetes in the United States

2024· article· en· W4405619227 on OpenAlexfundno aff
Grace Ellen Brannon, Tiffany B. Kindratt, Kyrah K. Brown

Bibliographic record

VenueJournal of Patient Experience · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
FundersMaternal and Child Health BureauHealth Resources and Services AdministrationYork UniversityU.S. Department of Health and Human Services
KeywordsDiabetes mellitusMedicineOdds ratioOddsConfidence intervalFamily medicineHealth careMedical Expenditure Panel SurveyPatient satisfactionGerontologyNursingInternal medicineLogistic regressionEndocrinology

Abstract

fetched live from OpenAlex

This study used the Medical Expenditure Panel Survey data (2010-2018) to examine associations between diabetes patients' satisfaction with their provider and ratings of healthcare received, diabetes care self-efficacy, and monitoring adherence among nonpregnant reproductive age women with diabetes. The sample included nonpregnant women of childbearing age (18-45) with diabetes mellitus (n = 767; weighted n = 1.3 million women). The results indicated that patients who reported that their usual care provider always asked/showed respect for medical, traditional, and alternative treatments that the person is happy with had 2.59 times greater odds (95% confidence interval [CI]:1.32-5.10) of giving high ratings of healthcare (8-10) compared to those whose provider did not show respect for treatments. Results also showed that patients who reported they were asked to decide between a choice of treatments had 1.76 greater odds (95% CI:1.03-3.01) of diabetes care monitoring adherence. Findings demonstrate the importance of patient-centered communication experiences in relation to diabetes care monitoring adherence. Implications of the findings for clinical encounters are discussed.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.213
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.371
Teacher spread0.347 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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