In-Depth Analysis of Patient-Clinician Cell Phone Communication during the WelTel Kenya1 Antiretroviral Adherence Trial
Bibliographic record
Abstract
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 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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".