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Record W2401054881 · doi:10.1177/1087054715624227

Comparison of Performance on ADHD Quality of Care Indicators: Practitioner Self-Report Versus Chart Review

2016· article· en· W2401054881 on OpenAlexaff
Megan Kathleen Gordon, Rebecca Baum, William Gardner, Kelly J. Kelleher, Joshua M. Langberg, William B. Brinkman, Jeffery N. Epstein

Bibliographic record

VenueJournal of Attention Disorders · 2016
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersNational Center for Advancing Translational SciencesNational Institute of Mental Health
KeywordsChartGuidelinePsychologyRating scaleMedicineClinical psychologyPsychiatryStatisticsDevelopmental psychology

Abstract

fetched live from OpenAlex

Objective: This study compared practitioner self-report of ADHD quality of care measures with actual performance, as documented by chart review. Method: In total, 188 practitioners from 50 pediatric practices completed questionnaires in which they self-reported estimates of ADHD quality of care indicators. A total of 1,599 charts were reviewed. Results: The percentage of patients for whom practitioners self-reported that they used evidence-based care was higher in every performance category when compared with chart review, including higher use of parent and teacher rating scales during assessment and treatment compared with chart review. Self-reported use of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) criteria during assessment was also higher than by chart review. The actual number of days until the first contact after starting medication was nearly three times longer than self-report estimates. Conclusion: Practitioners overreport performance on quality of care indicators. These differences were large and consistent across ADHD diagnostic and treatment monitoring practices. Practitioner self-report of ADHD guideline adherence should not be considered a valid measure of performance.

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)0.001
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 score0.032
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.071
GPT teacher head0.425
Teacher spread0.354 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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