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Record W2066251335 · doi:10.1080/15374416.2013.814541

Assessing Anxiety in Youth with the Multidimensional Anxiety Scale for Children

2013· article· en· W2066251335 on OpenAlexaff
Chiaying Wei, Alexandra Hoff, Marianne Aalberg, Jeremy S. Peterman, Philip C. Kendall, John Piacentini, James T. McCracken, John T. Walkup, Anne Marie Albano, Moira A. Rynn, Joel Sherrill, Dara Sakolsky, Boris Birmaher, Golda S. Ginsburg, Courtney Keeton, Elizabeth Gosch, Scott N. Compton, John S. March

Bibliographic record

VenueJournal of Clinical Child & Adolescent Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsChild, Adolescent and Family Mental Health
FundersNational Institute of Mental Health
KeywordsPsychologyAnxietySocial anxietyDiscriminant validityPanicClinical psychologySeparation anxiety disorderPanic disorderHarm avoidanceAnxiety disorderPsychiatryPsychometricsInternal consistencyPersonalityTemperament

Abstract

fetched live from OpenAlex

The present study examined the psychometric properties, including discriminant validity and clinical utility, of the youth self-report and parent-report forms of the Multidimensional Anxiety Scale for Children (MASC) among youth with anxiety disorders. The sample included parents and youth (N = 488, 49.6% male) ages 7 to 17 who participated in the Child/Adolescent Anxiety Multimodal Study. Although the typical low agreement between parent and youth self-reports was found, the MASC evidenced good internal reliability across MASC subscales and informants. The main MASC subscales (i.e., Physical Symptoms, Harm Avoidance, Social Anxiety, and Separation/Panic) were examined. The Social Anxiety and Separation/Panic subscales were found to be significantly predictive of the presence and severity of social phobia and separation anxiety disorder, respectively. Using multiple informants improved the accuracy of prediction. The MASC subscales demonstrated good psychometric properties and clinical utilities in identifying youth with anxiety disorders.

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)0.002
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 score0.080
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.051
GPT teacher head0.382
Teacher spread0.331 · 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

Citations134
Published2013
Admission routes1
Has abstractyes

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