MétaCan
Menu
Back to cohort
Record W1997845489 · doi:10.2147/ndt.s64136

Attention-deficit/hyperactivity disorder in postsecondary students

2014· review· en· W1997845489 on OpenAlexaff
Katharine Murkett, Wallace Smart, Kevin Nugent

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2014
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsFleming CollegeTrent UniversityUniversity of AlbertaUniversity of Lethbridge
Fundersnot available
KeywordsAttention deficit hyperactivity disorderMedicinePsychiatryAcademic achievementAttention deficitClinical psychologyPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

A PubMed review was conducted for papers reporting on attention-deficit/hyperactivity disorder (ADHD) in postsecondary students. The review was performed in order to determine the prevalence and symptomatology of ADHD in postsecondary students, to examine its effects on academic achievement, and discuss appropriate management. The prevalence of ADHD symptoms among postsecondary students ranges from 2% to 12%. Students with ADHD have lower grade point averages and are more likely to withdraw from courses, to indulge in risky behaviors, and to have other psychiatric comorbidities than their non-ADHD peers. Ensuring that students with ADHD receive appropriate support requires documented evidence of impairment to academic and day-to-day functioning. In adults with ADHD, stimulants improve concentration and attention, although improved academic productivity remains to be demonstrated. ADHD negatively impacts academic performance in students and increases the likelihood of drug and alcohol problems. Affected students may therefore benefit from disability support services, academic accommodations, and pharmacological treatment.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.008
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.030
GPT teacher head0.354
Teacher spread0.324 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations40
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueNeuropsychiatric Disease and TreatmentSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207