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Record W4281491824 · doi:10.1177/08295735221089457

Lessons Learned: Achieving Consensus About Learning Disability Assessment and Diagnosis

2022· article· en· W4281491824 on OpenAlexaff
Tricia S. Williams, Judith Wiener, C.G. Lennox, Maria Kokai

Bibliographic record

VenueCanadian Journal of School Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPsychologyProcess (computing)Learning disabilityMedical educationStatement (logic)Applied psychologyDevelopmental psychologyMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The current paper describes the process used for developing the Guidelines for Diagnosis and Assessment of Children, Adolescents, and Adults with Learning Disabilities-Consensus Statement and Supporting Documents, and the rationale for some of the decisions. The guidelines were developed by a cross-sectoral working group of psychologists who achieved a consensus on the criteria for diagnosis and the assessment process. We outline key features of the guidelines, describe topics where the group achieved consensus quickly and topics for which there was considerable debate (e.g., intelligence testing, ability/achievement discrepancy, and processing deficits). The group members shared information with each other about topics such as the advantages of early assessment, the importance of formally assessing effort and motivation, and assessment of culturally and linguistically diverse individuals. We conclude with the lessons learned and professional challenges regarding contextual influences on LD assessment and diagnosis and dissemination of research to practitioners.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0230.000

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 head0.144
GPT teacher head0.467
Teacher spread0.323 · 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.

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

Citations8
Published2022
Admission routes1
Has abstractyes

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