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Record W3148558978 · doi:10.1093/pch/pxab008

Implementation and evaluation of a curriculum on the assessment and treatment of disruptive behaviour disorders

2021· article· en· W3148558978 on OpenAlexafffundabout
Asif Doja, Tamara Pringsheim, Brendan F. Andrade, Lindsay Cowley, Sarah Healy, Tara Baron, Melissa Chan, Marielena Dibartolo, Karen W. Gripp, Sarah Manos, Jason A. Silverman, Hilary Writer, Daniel Gorman

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsIzaak Walton Killam Health CentreUniversity of AlbertaHospital for Sick ChildrenChildren's Hospital of WinnipegNOSM UniversityUniversity of OttawaUniversity of TorontoCentre for Addiction and Mental HealthAlberta Children's HospitalUniversity of CalgaryChildren's Hospital of Eastern Ontario
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenRoyal Bank of Canada
KeywordsConduct disorderCurriculumPsychiatryMedicinePsychologyMood disordersClinical psychologyAnxiety

Abstract

fetched live from OpenAlex

Disruptive behaviour disorders (DBDs)-which can include or be comorbid with disorders such as attention-deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder and disruptive mood dysregulation disorder-are commonly seen in paediatric practice. Given increases in the prescribing of atypical antipsychotics for children and youth, it is imperative that paediatric trainees in Canada receive adequate education on the optimal treatment of DBDs. We describe the development, dissemination, and evaluation of a novel paediatric resident curriculum for the assessment and treatment of DBDs in children and adolescents. Pre-post-evaluation of the curriculum showed improved knowledge in participants.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.420
Teacher spread0.369 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes3
Has abstractyes

Explore more

Same venuePaediatrics & Child Health→Same topicAutism Spectrum Disorder Research→French-language works237,207→