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Record W2965349128 · doi:10.5539/gjhs.v11n8p128

Health and Nutrition Issues Affecting Academic Involvement of Adult Learners in Literacy Programmes of Kogi State, Nigeria

2019· article· en· W2965349128 on OpenAlexvenueno aff
Linus Okechukwu Nwabuko, Eberechukwu Charity Eneh, Eunice R. Idakpo

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)StatisticLiteracyTest (biology)Descriptive statisticsMedicineMedical educationPsychologyHealth literacyGerontologyEnvironmental healthPedagogyHealth carePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: This study investigated health and nutrition issues affecting academic involvement of Adult learners in literacy programme in Kogi State, Nigeria. The specific purpose of the study was: to ascertain the extent health and nutrition affect academic involvement of adult learners in literacy programmes in Kogi State Nigeria. MATERIALS & METHOD: The design for the study was a descriptive survey design. A structured questionnaire was used to collect data which wereanalysed using mean scores and standard deviation while t-test statistic was used to test the hypothesis that guided the study. RESULTS: Results of the analysis showed among others health and nutrition issues such as chronic illness, poor nutrition and hunger affect academic involvement of adult learners. The results also showed that unhealthy adult learners do not feel happy in class during lessons or learning activities. CONCLUSION: Based on the finding, it was concluded that health and nutrition have some level of relationship with adult learners’ academic involvement to a high extent.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.324
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.414
Teacher spread0.385 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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