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Record W2788338087 · doi:10.1016/j.invent.2018.02.002

Navigating the development and dissemination of internet cognitive behavioral therapy (iCBT) for anxiety disorders in children and young people: A consensus statement with recommendations from the #iCBTLorentz Workshop Group

2018· article· en· W2788338087 on OpenAlexaff
Claire Hill, Cathy Creswell, Sarah Vigerland, Maaike H. Nauta, Sonja March, Caroline L. Donovan, Lidewij H. Wolters, Susan H. Spence, Jennifer Martin, Lori Wozney, Lauren F. McLellan, Leonie Kreuze, Karen Gould, Maral Jolstedt, Martina Nord, Jennifer L. Hudson, Elisabeth M. W. J. Utens, Jeroen Ruwaard, Casper J. Albers, Muniya Khanna, Anne Marie Albano, Eva Serlachius, Stefan Hrastinski, Philip C. Kendall

Bibliographic record

VenueInternet Interventions · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsIzaak Walton Killam Health Centre
FundersUniversity of ReadingNational Institute for Health and Care Research
KeywordsAnxietyThe InternetCognitive behavioral therapyCognitionPsychologyMedical educationClinical psychologyMedicineComputer sciencePsychiatryWorld Wide Web

Abstract

fetched live from OpenAlex

Initial internet-based cognitive behavioral therapy (iCBT) programs for anxiety disorders in children and young people (CYP) have been developed and evaluated, however these have not yet been widely adopted in routine practice. The lack of guidance and formalized approaches to the development and dissemination of iCBT has arguably contributed to the difficulty in developing iCBT that is scalable and sustainable beyond academic evaluation and that can ultimately be adopted by healthcare providers. This paper presents a consensus statement and recommendations from a workshop of international experts in CYP anxiety and iCBT (#iCBTLorentz Workshop Group) on the development, evaluation, engagement and dissemination of iCBT for anxiety in CYP.

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)0.224
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.224
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2240.142
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.003
Science and technology studies0.0050.005
Scholarly communication0.0060.007
Open science0.0080.012
Research integrity0.0150.021
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.389
Teacher spread0.355 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations47
Published2018
Admission routes1
Has abstractyes

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