Medication Education Provided to School‐Aged Children: A Systematic Scoping Review
Bibliographic record
Abstract
OBJECTIVES: To characterize the educational interventions regarding prescription and nonprescription medication use for school-aged children, we systematically reviewed evidence of programs available for this age group. METHODS: Searches in PubMed, CINAHL, EMBASE, ERIC, and International Pharmaceutical Abstracts were conducted. Search terms focused on: population education, school-age children, and medications. Studies were excluded if they were specific to a particular disease state or class of medication, drugs of misuse and illicit drugs. Data extraction included study design, location, educational intervention and duration, research methods, and main findings. RESULTS: We found 14 studies representing 8 separate projects. Six projects were identified in the gray literature. Projects ranged from individual sessions to national programs. Quantitative studies showed improvement in knowledge, medication literacy, and confidence. The adoption of medication education strategies was dependent on the educator's comfort level and beliefs regarding medication safety. CONCLUSIONS: Credible medication education resources are available and have been shown to improve students' knowledge. There remains a need for multifaceted implementation and evaluation strategies. Strategies and resources are available to implement interventions in communities to address medication education in school-age children. Frameworks should be used to facilitate the implementation of effective health promotion strategies around safe-medication use for school-aged children.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".