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Record W2171296482 · doi:10.1177/0829573512437024

The Importance of Symptom Validity Testing in Adolescents and Young Adults Undergoing Assessments for Learning or Attention Difficulties

2012· article· en· W2171296482 on OpenAlexaff
Allyson G. Harrison, Paul Green, Lloyd Flaro

Bibliographic record

VenueCanadian Journal of School Psychology · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyTest (biology)MalingeringClinical psychologyMedical diagnosisLearning disabilityNeuropsychologyNeuropsychological testingStimulantYoung adultNeuropsychological assessmentPsychiatryDevelopmental psychologyCognitionMedicine

Abstract

fetched live from OpenAlex

It is almost self-evident that test results will be unreliable and misleading if those undergoing assessments do not make a full effort on testing. Nevertheless, objective tests of effort have not typically been used with young adults to determine whether test results are valid or not. Because of the potential economic and/or recreational benefits of obtaining the diagnosis of attention deficit hyperactivity disorder (ADHD) or a learning disability (LD), concerns have been raised regarding the ease with which unimpaired young adults can feign either of these disorders to gain access to test accommodations, stimulant medication, or disability benefits. Much evidence has been presented recently regarding the need for symptom validity tests (SVTs) in assessment of college-aged students seeking diagnoses of LD and/or ADHD. Four cases are presented here in which intelligence and other test scores of young adults greatly underestimated their actual abilities, owing to poor effort that sometimes went undetected. Selected effort tests for use with young adults are discussed. Objective testing of effort is recommended to avoid misinterpreting invalid test data, which is why the use of effort tests is now standard practice in forensic neuropsychology.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.153
GPT teacher head0.415
Teacher spread0.262 · 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

Citations28
Published2012
Admission routes1
Has abstractyes

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