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Record W2922508364 · doi:10.1177/1073191119832657

Comparing Scores From Full Length, Short Form, and Adaptive Tests of the Social Interaction Anxiety and Social Phobia Scales

2019· article· en· W2922508364 on OpenAlexaff
Matthew Sunderland, Mohammad H. Afzali, Philip J. Batterham, Alison L. Calear, Natacha Carragher, Megan J. Hobbs, Alison Mahoney, Lorna Peters, Tim Slade

Bibliographic record

VenueAssessment · 2019
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyComputerized adaptive testingConcordanceAnxietySocial anxietyShort FormsDevelopmental psychologyClinical psychologyPsychometricsPsychiatry

Abstract

fetched live from OpenAlex

The current study developed and examined the performance of a computerized adaptive version of the Social Interaction Anxiety and Social Phobia Scales (SIAS/SPS) and compared results with a previously developed static short form (SIAS-6/SPS-6) in terms of measurement precision, concordance with the full forms, and sensitivity to treatment. Among an online sample of Australian adults, there were relatively minor differences in the performance of the adaptive tests and static short forms when compared with the full scales. Moreover, both adaptive and static short forms generated similar effect sizes across treatment in a clinical sample. This provides further evidence for the use of static or adaptive short forms of the SIAS/SPS rather than the lengthier 20-item versions. However, at the individual level, the adaptive tests were able to maintain an acceptable level of precision, using few items as possible, across the severity continua in contrast to the static short forms.

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 score0.091
Threshold uncertainty score0.695

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 head0.051
GPT teacher head0.372
Teacher spread0.321 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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