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What do children with cancer know about their medications?

2011· article· en· W1974023969 on OpenAlexaff
Thomas M. MacDonald, David W. Macdonald, Bruce Crooks, C. Collicott

Bibliographic record

VenuePharmacy Practice · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsMedicineLiteracyFamily medicineAge appropriatePediatricsQualitative researchPsychologyDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.011
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.115
GPT teacher head0.511
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2011
Admission routes1
Has abstractyes

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