IS IT A SURPRISE? SHOCKING SUB-OPTIMAL MEDICATION SAFETY IN CHILDREN WITH MEDICAL COMPLEXITY
Bibliographic record
Abstract
Abstract BACKGROUND There are increasing numbers of children with medical complexity (CMC) worldwide. CMC make multiple transitions between healthcare settings and providers; frequently receiving multiple medications. While the risk of medication error in children is recognized, data on CMC, perhaps one of the most vulnerable groups, is lacking. OBJECTIVES To explore parental knowledge, perceptions and behaviours around medication safety and to identify and describe recalled medication errors in order to guide future efforts in quality and safety. DESIGN/METHODS Study participants: Parents of CMC followed in a university hospital based specialized clinic. Study design: Cross-sectional prospective survey, administered by face-to-face interview. Study methods: Because no pre-existing suitable validated survey tool was identified, a 35-item bilingual survey was developed using quantitative and qualitative methods. Testing for face and construct validity was conducted. Consecutive parents of CMC presenting to a designated clinic were approached for study participation. Following informed consent, parents were interviewed for 12–15 minutes with responses entered into a web-based secure database with integrated statistical analysis for data interpretation. Additional diagnostic, verification and healthcare utilization data were collected from the medical chart. RESULTS Complete data was collected for 51 CMC; 77% of respondents were mothers; 3 parents declined to participate. Children, 35% female, ranged in age between 6 months and 17 years. Polypharmacy was common; 77% of CMC had 3 or more medications, 53% had five or more daily medications. Only 37% of parents reported having a complete list of their child’s medications; less than quarter of those administering liquid medications were able to report the actual dose/concentration. Almost two-thirds of parents were worried that their child might have a medication error in the future with 45% recalling at least one medication error in the past. Of these parents, 76% reported that the error occurred during inpatient hospital stay, most commonly due to a dosage error. The reported impact on the child due to the error included life-threatening situations. CONCLUSION Medication error in CMC is perceived and reported as a significant risk by parents. Nonetheless, only a minority were able to show a list of their child’s medications. The rate of reported errors suggests that existing prevention practices remain suboptimal. Further study in other care settings is warranted to confirm these findings and to determine which strategies may be most effective in reducing the risk of medication error in CMC.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".