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Record W2736944357 · doi:10.1177/2333794x17719803

Validation of a Cystic Fibrosis Medication Knowledge Questionnaire

2017· article· en· W2736944357 on OpenAlexaff
Beverly FitzPatrick, John Hawboldt, Mary Jane Smith, Tiffany Lee

Bibliographic record

VenueGlobal Pediatric Health · 2017
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSt. John’s Health Sciences CentreMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineCronbach's alphaCystic fibrosisContent validityFamily medicineMedication adherenceTest (biology)Clinical psychologyPsychometricsInternal medicine

Abstract

fetched live from OpenAlex

Low adherence to cystic fibrosis (CF) treatment is associated with poor health outcomes, while knowledge of the disease and medication regimen can positively influence adherence. This study’s purpose was to develop and validate a questionnaire to help determine CF medication knowledge of pediatric patients and caregivers. Our questionnaire had 37 items: 22 selected-response and 15 open-response questions. We used validation processes from the Standards for Educational and Psychological Testing. CF experts analyzed validity evidence based on content. Then, the questionnaire was field tested with 17 pediatric patients and 18 caregivers. The correlation between age and medication knowledge was medium ( r = .33), but was not significant ( P = .189). Cronbach’s α for the overall test was .84. Participants thought the questionnaire was important and suitable, with a few minor suggestions to improve wording. Strong validity evidence indicates the questionnaire could be used to assess medication knowledge and allow more personalized education to improve adherence.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.390
Teacher spread0.366 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2017
Admission routes1
Has abstractyes

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