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Record W2126014465 · doi:10.1371/journal.pone.0046033

In-Depth Analysis of Patient-Clinician Cell Phone Communication during the WelTel Kenya1 Antiretroviral Adherence Trial

2012· article· en· W2126014465 on OpenAlexaff
Mia L. van der Kop, Sarah Karanja, Lehana Thabane, Carlo A. Marra, Michael H. Chung, Lawrence Gelmon, Joshua Kimani, Richard Lester

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityBC Centre for Disease Control
FundersCenters for Disease Control and PreventionU.S. President’s Emergency Plan for AIDS Relief
KeywordsRate ratioMedicineIntervention (counseling)Incidence (geometry)ResidencePhoneRandomized controlled trialText messageYoung adultAntiretroviral therapyDemographyViral loadHuman immunodeficiency virus (HIV)Internal medicineFamily medicineConfidence intervalPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The WelTel Kenya1 trial demonstrated that text message support improved adherence to antiretroviral therapy (ART) and suppression of HIV-1 RNA load. The intervention involved sending weekly messages to patients inquiring how they were doing; participants were required to respond either that they were well or that there was a problem. OBJECTIVES: 1) Describe problems participants identified through mobile phone support and reasons why participants did not respond to the messages; 2) investigate factors associated with indicating a problem and not responding; and 3) examine participant perceptions of the intervention. DESIGN: Secondary analysis of WelTel Kenya1 trial data. METHODS: Reasons participants indicated a problem or did not respond were extracted from the study log. Negative binomial regression was used to determine participant characteristics associated with indicating a problem and non-response. Data from follow-up questionnaires were used to describe participant perceptions of the intervention. RESULTS: Between 2007 and 2009, 271 participants generated 11,873 responses; 377 of which indicated a problem. Health issues were the primary reason for problem responses (72%). Rural residence (adjusted incidence rate ratio [IRR] 1.96; 95%CI 1.19-3.25; p=0.009 and age were associated with indicating a problem (adjusted IRR 0.63 per increase in age group category; 95%CI 0.50-0.80; p<0.001). Higher educational level was associated with a decreased rate of non-response (adjusted IRR 0.81; 95%CI 0.69-0.94; p=0.005). Of participants interviewed, 62% (n=129) stated there were no barriers to the intervention; cell phone issues were the most common barrier. Benefits included reminding patients to take medication and promoting a feeling that "someone cares". CONCLUSIONS: The WelTel intervention enabled frequent communication between clinicians and patients during the WelTel Kenya1 trial. Many patients valued the service for the support it provided, with health-related concerns comprising the majority of problems identified by participants. Few sociodemographic characteristics were associated with participant engagement in the intervention.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.395
Teacher spread0.296 · 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

Citations63
Published2012
Admission routes1
Has abstractyes

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