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Record W2809882574 · doi:10.1093/qjmed/hcw127.027

P091 <break /> Development of a disease knowledge questionnaire for patients with interstitial lung disease

2016· article· en· W2809882574 on OpenAlexaff
Julie Morisset, Bruno‐Pierre Dubé, Kaïssa de Boer, Robert Brownell, Kerri A. Johannson, Jean Bourbeau, Harold R. Collard

Bibliographic record

VenueQJM · 2016
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcGill University Health CentreUniversity of CalgaryUniversity of OttawaCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsInterstitial lung diseaseDiseaseMedicineLungLung diseaseInternal medicine

Abstract

fetched live from OpenAlex

Background: Disease education plays an important role in the comprehensive care of patients with interstitial lung disease (ILD). No tool has been developed to assess disease knowledge in these patients. Aim: To develop a bilingual (French and English), reliable and easy to use questionnaire assessing disease knowledge in patients with ILD. Methods: An initial questionnaire of 40 potential items was generated after a comprehensive literature review. Twenty content experts qualitatively and quantitatively reviewed each item for inclusion in the final questionnaire. Initially developed in French, it was translated to English using the forward and backward translation method. The questionnaire was pre-tested in 20 ILD patients of each language to assess for the readability and clarity of the questions. Forty additional patients (20 French, 20 English) completed the questionnaire twice to determine its internal consistency and test-retest reliability. Results: The final questionnaire included 18 items rated as essential by experts (content validity ratio = p < 0.05). Internal consistency of the questionnaire was satisfactory, with Cronbach’s alphas of 0.76 in French and 0.77 in English. Test-retest reliability using the intraclass correlation coefficient was 0.91 in French and 0.74 in English.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.410
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueQJMSame topicInterstitial Lung Diseases and Idiopathic Pulmonary FibrosisFrench-language works237,207