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Assessment of Intellectual Functioning among Children with Neurodevelopmental Disorders: Challenges and Implications Beyond the Clinical Practice

2018· article· en· W2784251628 on OpenAlexvenueno aff
Mihaela Hristova, Harieta Manolova, Svetla Nikolaeva Staykova, Galina Markova

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2018
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyClinical PracticeIntellectual disabilityDevelopmental psychologyClinical psychologyPsychiatryMedicineNursing

Abstract

fetched live from OpenAlex

Early and precise identification of neurodevelopmental disorders together with provision of adequate and timely interventions remain increasingly important tasks for multidisciplinary clinical teams. А central component in this process of comprehensive clinical evaluation is the assessment of children’s intellectual functioning. Intelligence test results represent a central component in the decision making process of determining a child’s future in terms of: qualification for special education, access to social welfare support, placement in therapeutic programs, etc. Clinical results yielded through the application of standardized intelligence assessment instruments (WISC, K-ABC, RPM), and especially the overall quantitative measure of cognitive ability (IQ) have become a central, and sometimes the only measure taken into consideration when determining a child’s level of functioning. Together with some distinct benefits, this practice places many children at risk of being underestimated and calls for revision and modification of standard assessment procedures. In line with these considerations, authors raise for discussion traditional approaches to diagnostics of intellectual functioning, highlighting some challenges, emerging from the constitutive particularities in the cognitive functioning of children with neurodevelopmental disorders. An attempt for identifying areas for further improvement alongside with research-informed recommendations for a contemporary, individualized and sensitive to the specifications of children with neurodevelopmental disorders assessment practice are outlined at the end of this paper. According to the authors’ opinion, exploration of the topic provides an important opportunity to advance the understanding of clinicians, primary healthcare professionals, educators and other professionals involved in supporting children with developmental deficits.

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)0.015
metaresearch head score (Gemma)0.047
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.348
Teacher spread0.302 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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