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Record W4390112613 · doi:10.58578/alsystech.v2i1.2372

Substance Abuse Effect On Cognitive Ability Of Selected Youngsters Studying In Gwadabawa, Sokoto State, Nigeria

2023· article· en· W4390112613 on OpenAlexaboutno aff
Ishaka Tambari, Mustapha Olanrewaju Aliyu, Bello Sulaiman

Bibliographic record

VenueALSYSTECH Journal of Education Technology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionSubstance abuseAbsenteeismAffect (linguistics)PsychologyClinical psychologyPsychiatryMedicineSocial psychology

Abstract

fetched live from OpenAlex

Substance abuse is among the major issues affecting schools and education in Nigeria. Many substances affect the mind and whole body of youngsters negatively leading to hospitalizations, school drop-outs, absenteeism and other effects that affect education at whole. The objective of this study was to assess the effect of substance abuse on cognitive potential of some schooling youngsters in Gwadabawa, Sokoto State Nigeria. 15 young people that abuse substances and are attending schools, and 15 normal people in Gwadabawa, Sokoto state, Nigeria were recruited in this work and were assessed with Montreal cognitive Assessment standard methods to evaluate the cognitive domain of the participants. The result reveals substance abusers scored 357 ± 7.0, while 396.0 ± 10.0 was scored by non-substance abusers; indicating that substance abusers show comparatively lower cognitive ability compared to the control youngsters enrolled in this work. The results indicate that the substances been abused by the young people reduce their cognitive ability (a prelude of cognitive domain of the participants of the study) and in turn could inflict their academic performances as well. Thus, it is pertinent to help young ones shun substances through awareness creation, counselling, strict laws, drug education and relations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
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 head0.023
GPT teacher head0.309
Teacher spread0.286 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes1
Has abstractyes

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