What do children know about medications? A review of the literature to guide clinical practice.
Bibliographic record
Abstract
OBJECTIVE: To guide physicians in their communications with children about medications. QUALITY OF EVIDENCE: PubMed, EMBASE, and the Cochrane Library were searched from 1980 up to August 2009 for qualitative and quantitative research that investigated children's knowledge of and beliefs about medications (levels of evidence II and III). Findings presented relate to healthy children aged 6 to 12 years old unless stated otherwise. MAIN MESSAGE: In order to improve children's use of medicine, experts suggest that physicians communicate directly with children about medications, instead of communicating only with parents or caregivers. Children as young as 6 years old form opinions about medications, and many of these opinions persist in the adult population. This article reviews what we know about how children identify medication; children's fear of medication; how they believe medication works; and their understanding of the medication-related concepts of medication efficacy, side effects, and treatment compliance. This knowledge will help physicians communicate more effectively with children about their medications. CONCLUSION: Family physicians can help children understand why they take medicine and how to use it appropriately starting at an early age. This early training might affect their medication-taking behaviour throughout their adult lives. Studies in Canada are needed to further understand children's beliefs about medication and to see if these beliefs correlate with international data.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".