Perceptions of HIV infected patients on the use of cell phone as a tool to support their antiretroviral adherence; a cross-sectional study in a large referral hospital in Kenya
Bibliographic record
Abstract
BACKGROUND: Clinical trials were conducted to assess the feasibility of using a cell phone text messaging-based system to follow up Human Immunodeficiency Virus (HIV) infected patients on antiretroviral (ARTs) and assess for improved adherence to their medication. However there is need to evaluate the perceptions of the HIV infected patients towards the use of these cell phones in an effort to better aid in the clinical management of their HIV infection. The objective of this study was therefore to determine the perceptions of HIV infected patients on the use of cell phone text messaging as a tool to support adherence to their ART medication. METHODS: A cross sectional survey was conducted among patients receiving Highly Active Anti-Retroviral Therapy (HAART) at the Kenyatta National Hospital Comprehensive Care Clinic in Nairobi between May and July, 2011. Pre-tested questionnaires were used to collect the socio-demographic and perceptions data. The recruitment of the participants was done using the random probability sampling method and statistical analysis of data performed using Statistical Package for Social Sciences (SPSS) version 16.0. RESULTS: A total of 500 HIV infected patients (Male-107, Female-307) aged 19-72 years were interviewed. The majority of individuals (99%) had access to cell phones and 99% of the HIV infected patients interviewed supported the idea of cell phone use in management of their HIV infection. A large proportion (46%) claimed that they needed cell phone access for medical advice and guidance on factors that hinder their adherence to medication and only 3% of them needed it as a reminder to take their drugs. The majority (72%) preferred calling the healthcare provider with their own phones for convenience and confidential purposes with only 0.4% preferring to be called or texted by the health care provider. Most (94%), especially the older patients, had no problem with their confidentiality being infringed in the process of the conversation as per the bivariate analysis results. CONCLUSION: Cell phone communications are acceptable and in fact preferable over cell phone reminders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".