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Record W2087781315 · doi:10.2147/hiv.s29954

Considerations in using text messages to improve adherence to highly active antiretroviral therapy: a qualitative study among clients in Yaoundé, Cameroon

2012· article· en· W2087781315 on OpenAlexafffund
Lawrence Mbuagbaw, Bonono-Momnougui, Lehana Thabane

Bibliographic record

VenueHIV/AIDS - Research and Palliative Care · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersCanadian Institutes of Health Research
KeywordsAntiretroviral therapyQualitative researchHuman immunodeficiency virus (HIV)MedicineAntiretroviral treatmentFamily medicinePsychologyViral loadSociologySocial science

Abstract

fetched live from OpenAlex

Poor adherence to highly active antiretroviral therapy (HAART) is a major hindrance to the reduction of mortality and morbidity due to HIV. This qualitative study used focus groups to explore the views and experiences of HIV patients on HAART with adherence reminders, especially the text message (SMS [short message service]). The ethnographic data obtained were used to design a clinical trial to assess the effect of motivational text messages versus usual care to enhance adherence to HAART among HIV patients in Yaoundé, Cameroon. Participants appreciated the idea of a timely SMS reminder, and cited the physician as a role model. They expressed concerns about privacy. Long-term life goals were a motivating factor to adhere. Overall, text messaging was viewed positively as a tool with a dual function of reminder and motivator. Messages coming from the attending physician may have a stronger impact. Trials investigating the use of text messages to improve adherence to HAART need to consider the content and timing of SMS, taking into account technical challenges and privacy.

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)0.013
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.209
GPT teacher head0.504
Teacher spread0.295 · 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 designQualitative
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

Citations39
Published2012
Admission routes2
Has abstractyes

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