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Record W3187067952 · doi:10.1186/s40814-021-00879-z

Assessing the feasibility of a classroom-based visual attention training program targeting academics for students with extremely low IQ

2021· article· en· W3187067952 on OpenAlexafffund
Catherine Archambault, Domenico Tullo, Emma Clark, Jocelyn Faubert, Armando Bertone

Bibliographic record

VenuePilot and Feasibility Studies · 2021
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversité de MontréalMcGill University
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of Canada
KeywordsTraining (meteorology)PsychologyMathematics educationMedical educationMultimediaPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: This feasibility study investigated the viability of implementing a cognitive-based training program (NeuroTracker) and assessing its potential effects on academic performance for adolescents with extremely low IQ. METHODS: = 13.89) completed 15 training sessions on either the NeuroTracker or an active control task; math and reading performance were assessed using clinically validated instruments before and after training. Recruitment and retention rates, adherence, and properties of the academic measures were assessed. RESULTS: All recruited participants completed 15 training sessions within a 6-week period. Eighty-three percent of participants meeting initial inclusion criteria completed all stages of the study from baseline to post-intervention assessments. Some limitations of the academic measures were identified. CONCLUSIONS: Results suggest that implementing NeuroTracker as a classroom-based intervention and using clinically validated outcome measures is feasible with this population.

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)0.002
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 score0.050
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.416
GPT teacher head0.517
Teacher spread0.101 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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