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Record W2998513798 · doi:10.5539/gjhs.v11n14p115

Gender Differences in Stress Perception among Special Education Students

2019· article· en· W2998513798 on OpenAlexvenueno aff
Michael Eskay, Florence Ijeoma Arumede, Annastasia Uchenna Eneh, Jane Ogoma Aja

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionStress (linguistics)PsychologyData collectionNull hypothesisSpecial educationTest (biology)Descriptive researchDescriptive statisticsMedical educationApplied psychologyClinical psychologyMathematics educationMedicineSociologySocial scienceMathematics

Abstract

fetched live from OpenAlex

This study examined gender differences in stress perception among special education students in South-East Nigerian Universities. One research question and a null hypothesis guided the study. The study design was a descriptive survey. The study sampled 800 students enrolled in special education. The instrument for collection of data was a questionnaire which measures the perception of stress in students. The research data were analyzed using mean, standard deviation and t-test. The finding of the study revealed that there was no significant between male and female academic stress. For affected students of special education to be stress-free, the collaboration of special education experts and educational stakeholders is necessary for assisting such students.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.483
Teacher spread0.392 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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