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Emotional Intelligence as a Predictor of Research Skills Acquisition Among University Students with Intellectual Disabilities in Calabar, Nigeria

2025· article· en· W4412055181 on OpenAlexvenueno aff
Chidirim Esther Nwogwugwu, Henrietta Uchegbue, Sylvia Victor Ovat, Nnenna Kalu Uka, Julius Michael Egbai, Patricia Ebere Chilebe Iwuala, Anthony Pius Effiom, Helen Akpama Andong, Richard Ayuh Ojini, Mayen Ndaro Igajah, Margaret Sylvanus Umoh, Remi Modupe Omoogun, Mokutima Ekpo, Unimke Sylvester Akongi, Ogbaji Dominic Ipuole, Charles Utsu Ushie

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2025
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEmotional intelligencePsychologyIntellectual disabilityMathematics educationDevelopmental psychologyMedical educationApplied psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Aim: Intellectual disability is characterized by significant limitations in intellectual functioning and adaptive behavior originating before age 18 (AAIDD, 2010). In higher education, these students often require individualized support, yet inclusive practices in Nigerian universities remain underdeveloped. This study examines the predictive relationship between emotional intelligence and research skills acquisition among university students with intellectual disabilities at the University of Calabar (UNI.CAL) and the University of Cross River (UNICROSS), Cross River State, Nigeria. Five study objectives were stated to guide the research. Five research questions were formulated, and three hypotheses were stated. Literature was reviewed based on the variables under study, as research gaps were also stated. Method: The study adopted a correlational survey research design. The area of the study is Cross River State, South-South, Nigeria. The study population consists of all 20,030 final-year undergraduate students with intellectual disabilities in inclusive departments of the University of Calabar and the University of Cross River State, offering special education or support for students with disabilities. A purposive sampling technique was used to select students identified with intellectual disabilities. A sample size of 401 participants was selected based on accessibility and consent. The instrument for data collection was a questionnaire. Cronbach Alpha reliability coefficient method was used in establishing the reliability index of .82. Results of the research questions were presented using frequency counts, percentages, mean and standard deviation, and Pearson's Product Moment Correlation, Multiple linear regression, and Independent t-test were used to analyze the research question and hypotheses. Results: The results revealed that emotional intelligence significantly contributes to developing research skills in students with intellectual disabilities. Emotional competencies such as self-awareness, motivation, and interpersonal sensitivity are essential tools in enabling these students to participate fully in research activities. Hence, emotional intelligence components collectively predict research skills acquisition. There is a significant difference between male and female students with intellectual disabilities in their level of research skills acquisition. Conclusion: This research concluded that there is a statistically significant relationship between emotional intelligence and the ability to develop and perform research tasks. It affirms the critical role emotional intelligence plays not just in social functioning but also in academic productivity, especially among students who often face exclusion or limited support. Recommendation: Universities should incorporate emotional intelligence training into their special education and general academic programs to build students’ self-efficacy and research competence.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.045
GPT teacher head0.384
Teacher spread0.338 · 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.

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
Published2025
Admission routes1
Has abstractyes

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