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Record W2092320993 · doi:10.1177/1073191109348590

Examining Unusual Digit Span Performance in a Population of Postsecondary Students Assessed for Academic Difficulties

2009· article· en· W2092320993 on OpenAlexaff
Allyson G. Harrison, Yoni Rosenblum, Shannon E. Currie

Bibliographic record

VenueAssessment · 2009
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyMemory spanPopulationPostsecondary educationSpan (engineering)Digit ratioDevelopmental psychologyClinical psychologyHigher educationCognitionPsychiatryWorking memoryGeneticsDemography

Abstract

fetched live from OpenAlex

Methods of identifying poor test-related motivation using the Wechsler Adult Intelligence Scale Digit Span subtest are based on identification of performance patterns that are implausible if the test taker is investing full effort. No studies to date, however, have examined the specificity of such measures, particularly when evaluating persons with either known or suspected learning or attention disorders. This study investigated performance of academically challenged students on three measures embedded in the Wechsler Adult Intelligence Scale-III, namely, low Digit Span, high Vocabulary-Digit span (Voc-DS), and low Reliable Digit Span scores. Evaluating subjects believed to be investing full effort in testing, it was found that both Digit Span and Reliable Digit Span had high specificity, although both showed relatively lower sensitivity. In contrast, VOC-DS was especially weak in both sensitivity and specificity, with an apparent false positive rate of 28%. Use of VOC-DS is therefore not appropriate for those with a history of learning or attention problems.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.442
Teacher spread0.332 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

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