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Record W883606204 · doi:10.1016/j.pmedr.2015.06.010

Correlates of mobile phone use in HIV care: Results from a cross-sectional study in South Africa

2015· article· en· W883606204 on OpenAlexaff
Naieya Madhvani, Elisa Longinetti, Michele Santacatterina, Birger C. Forsberg, Ziad El‐Khatib

Bibliographic record

VenuePreventive Medicine Reports · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversity of Ottawa
Fundersnot available
KeywordsMedicinePsychological interventionMarital statusLogistic regressionCross-sectional studyMobile phoneFamily medicineHuman immunodeficiency virus (HIV)GerontologyEnvironmental healthPopulationNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: Human Immunodeficiency Virus (HIV) is a major disease burden worldwide. Challenges include retaining patients in care and optimizing adherence to Antiretroviral Therapy (ART). One possible solution is using mobile phones as reminder tools. The main aim of our study was to identify patient demographic groups least likely to use mobile phones as reminder tools in HIV care. DESIGN: The data came from a cross-sectional study at the Chris Hani Baragwanath Hospital, Soweto Township, South Africa. METHODS: A comprehensive questionnaire was used to interview 883 HIV infected patients receiving ART. Logistic regression analysis was performed to identify the influence of age, gender, education level, marital status, number of sexual partners in the last three months, income level, and employment status on the use of mobile phone as reminders for clinic appointments and taking medication. RESULTS: Patient groups significantly associated with being less likely to use mobile phones as clinic appointment reminders were: a) patients 45 years or older, b) women, and c) patients with only primary or no schooling level. Patient groups significantly associated with being less likely to use mobile phones as medication reminders were: a) patients 35 years or older and b) patients with a lower monthly income. CONCLUSIONS: In this setting being a woman, of older age, lower education, and socio-economic level were risk factors for the low usage of mobile phones as reminder aids. Future studies should assimilate reasons for this, such that patient-specific barriers to implementation are identified and interventions can be tailored.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.050
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.102
GPT teacher head0.438
Teacher spread0.336 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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