What do children with cancer know about their medications?
Bibliographic record
Abstract
OBJECTIVE: To explore the health literacy of children diagnosed with Acute Lymphoblastic Leukemia (ALL) through their knowledge of their medications. METHODS: Within the Basic Interpretive approach to qualitative research, semi-structured interviews were conducted with children from ages 6 to18 years (n=16) between May and September 2009 to determine their knowledge of medication properties, medication habits and medication teaching. REB approval was obtained. RESULTS: The younger children (mean age 7.5 years) correctly answered, on average, 51% of the questions on colour, 26% of the questions on name, 25% of the questions on frequency, and 8% of the questions on the purposes of their medications. The older children (mean age 16 years) scored at least 35% higher for each characteristic. All of the younger children reported that physicians consistently directed medication education to parents only, and that the younger children were rarely present during these sessions. 13 of the 16 children stated that they want to learn more about and be more involved in education sessions addressing their medications. CONCLUSIONS: Children with ALL at the IWK Health Centre do not have a good knowledge of their medications, however most children expressed that they want to know more about their medications.
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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 | 0.010 |
| 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 | 0.001 |
| 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".