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Record W3150865158 · doi:10.1007/s12207-021-09406-0

Comparability and Validity of the Online and In-Person Administrations of the Inventory of Problems-29

2021· article· en· W3150865158 on OpenAlexaff
Luciano Giromini, Claudia Pignolo, Gerald Young, Eric Y. Drogin, Alessandro Zennaro, Donald J. Viglione

Bibliographic record

VenuePsychological Injury and Law · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsYork University
FundersUniversità degli Studi di Torino
KeywordsComparabilityEquivalence (formal languages)PsychologyTest (biology)Face validityPencil (optics)Test validityPsychometricsApplied psychologyClinical psychologyMathematicsEngineering

Abstract

fetched live from OpenAlex

Abstract While the psychometric equivalence of computerized versus paper-and-pencil administration formats has been documented for some tests, so far very few studies have focused on the comparability and validity of test scores obtained via in-person versus remote administrations, and none of them have researched a symptom validity test (SVT). To contribute to fill this gap in the literature, we investigated the scores of the Inventory of Problems-29 (IOP-29) generated by various administration formats. More specifically, Study 1 evaluated the equivalence of scores from nonclinical individuals administered the IOP-29 remotely ( n = 146) versus in-person via computer ( n = 140) versus in-person via paper-and-pencil format ( n = 140). Study 2 reviewed published IOP-29 studies conducted using remote/online versus in-person, paper-and-pencil test administrations to determine if remote testing could adversely influence the validity of IOP-29 test results. Taken together, our findings suggest that the effectiveness of the IOP-29 is preserved when alternating between face-to-face and online/remote formats.

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)0.017
metaresearch head score (Gemma)0.096
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.096
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.185
GPT teacher head0.437
Teacher spread0.252 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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