Instructions for masking the taste of medication for children
Bibliographic record
Abstract
BACKGROUND: Medications that taste unpleasant can be a struggle to administer to children, most often resulting in low adherence rates. Pictograms can be useful tools to improve adherence by conveying information to patients in a way that they will understand. METHODS: One-on-one structured interviews were conducted with parents/guardians and with children between the ages of 9 and 17 years at a pediatric hospital. The questionnaire evaluated the comprehension of 12 pictogram sets that described how to mask the taste of medications for children. Pictograms understood by >85% of participants were considered validated. Short-term recall was assessed by asking participants to recall the meaning of each pictogram set. RESULTS: There were 51 participants in the study-26 (51%) were children aged 9 to 17 years and 25 (49%) were parents or guardians. Most children (54%) had health literacy levels of grade 10 or higher. Most parents and guardians (92%) had at least a high school health literacy level. Six of the 12 pictogram sets (50%) were validated. Eleven of 12 pictogram sets (92%) had a median translucency score greater than 5. All 12 pictogram sets (100%) were correctly identified at short-term recall and were therefore validated. CONCLUSION: The addition of validated illustrations to pharmaceutical labels can be useful to instruct on how to mask the taste of medication in certain populations. Further studies are needed to assess the clinical impact of providing illustrated information to populations with low health literacy.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".