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Record W2062714458 · doi:10.1080/09084281003715642

Symptom Exaggeration in Post-Secondary Students: Preliminary Base Rates in a Canadian Sample

2010· article· en· W2062714458 on OpenAlexaffabout
Allyson G. Harrison, M. J. Edwards

Bibliographic record

VenueApplied Neuropsychology · 2010
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsExaggerationPsychologySample (material)Test (biology)Clinical psychologyStandardized testPsychiatryMathematics education

Abstract

fetched live from OpenAlex

Recent studies conducted at American post-secondary institutions report that a high proportion of college students seeking evaluations for either attention-deficit/hyperactivity disorder or learning disorders fail symptom validity tests (SVTs), calling into question the validity of their performance on standardized assessment measures. The current study undertook to investigate the rate of SVT failure in a Canadian post-secondary sample, drawing on assessment data from a large regional assessment facility. Evaluating the data from 144 consecutively tested students, the present study found that 14.6% of students failed an SVT, and those who failed returned lower scores on many other assessment measures compared with those who passed. These findings indicate that the rate of symptom exaggeration or low test-taking effort may be lower in Canadian samples than in U.S. samples but still represents a substantial number of students. Recommendations and suggestions for future directions are discussed.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.025
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0040.002
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.357
Teacher spread0.328 · 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

Citations79
Published2010
Admission routes2
Has abstractyes

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