Randomized Controlled Trial Evaluating Pictogram Augmentation of HIV Medication Information
Bibliographic record
Abstract
BACKGROUND: Antiretroviral therapy for the management of HIV typically requires the chronic use of 3 or more medications. As such, patients with HIV are required to manage complex dosing schedules and are at risk of multiple potential adverse effects. The use of pictograms on medication vials as a means of improving patients' understanding of medication information has been shown to positively influence understanding and adherence compared to those using text alone. OBJECTIVE: To determine whether pictograms (Pharmaglyph) increase patient recall of targeted information associated with HIV medications and whether patients can interpret the intended meaning of pictograms that they had not seen previously. METHODS: A randomized, controlled trial was conducted in HIV-positive patients aged 19 years or older who were receiving a new prescription for an antiretroviral medication from the ambulatory pharmacy at St. Paul's Hospital in Vancouver, British Columbia, Canada. Participants were randomized to receive either pictogram-enhanced medication information or standard counseling. At the first follow-up visit, each patient's recall of the medication information was evaluated, and differences between groups were compared. RESULTS: Eighty-two subjects were randomized, 40 to the intervention group and 42 to the control arm. The mean (SD) number of HIV medications was nearly equal between the intervention and control groups: 3.0 (1.5) and 3.1 (1.4), respectively. After a mean of 34 days, 33 patients in the intervention arm and 39 in the control arm completed the study. The majority (88%) of the targeted pieces of information in the intervention group were correctly identified at follow-up, compared to only 2% in the control group (Fisher exact test; p < 0.0001). CONCLUSIONS: Pictograms improve the recall of targeted medication information among patients receiving antiretroviral therapy for HIV management; however, this appears to be dependent on the fact that these patients received a verbal explanation of each pictogram prior to use.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".