Protocol for Using Mobile Phone Text Messaging to Improve Adherence to Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
Interventions for improving medication adherence that can become part of patients’ daily life are critical as the therapy is lifelong. Medication adherence is the cornerstone of highly active antiretroviral therapy (HAART). With the blooming of cell phone ownership worldwide, research using mobile phone strategies to improve HAART adherence has increased. In addition, there are over 28 million mobile phone subscribers in Taiwan (Institution of Information Industry, 2011). We carried out a literature review selection using the population, intervention, comparison and outcome(s) (PICO) format. We used evidence gathered by evidenced-based methods to construct a clinical guideline. Evidence from two randomized control trials and two systematic reviews that used the mobile phone as the intervention was included in the protocol. A protocol for using mobile phone texting as an intervention to improve adherence was thereby established. Key words: Mobile phone; Adherence; Intervention; HAART
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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 | 0.034 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.124 | 0.024 |
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".