Hypothetical Analysis of the Effects of Climate Change on Mental Health of Undergraduates in Alex-Ekwueme Federal University, Ebonyi State of Nigeria
Bibliographic record
Abstract
INTRODUCTION: Mental health (MH) effect caused by climate change, particularly on adolescents and adults is a call for concern. This study aimed at exploring the effects of climate change on the mental health of Undergraduates of Alex Ekwueme Federal University, Ndufu-Alike, Ebonyi State. METHODS: An institutional based cross-sectional study was adopted. The population consisted of 10,000 students. The sample for the study consisted 216 undergraduates. The instrument for the study was a self-structured questionnaire titled: Effect of Climate Change on Mental Health (ECCMHQ). Data was analyzed using bivariate correlational analysis to determine the association of climate change with the effects of MH, while structural equation modelling was used to test the hypotheses. RESULTS: The findings showed that climate change was positively correlated with stress disorder (r = 0.25, p <.01), anxiety (r = 0.32, p <.01), depression (r = 0.26, p <.01), trauma (r = 0.28, p <.01), substance abuse (r = 0.30, p <.01), suicidal ideation (r = 0.25, p <.01), fatigue (r = 0.27, p <.01) and suicidal guilt (r = 0.17, p <.05). There was no evidence of a correlation between climate change and post trauma (r = 0.12, p =.45) and a negative correlation with trauma (r = -0.16, p <.05). CONCLUSION: The study concluded that climate change can lead to tremendous mental health effects such as anxiety, PTSD, apocalypse, fear with their consequential chronic psychological dysfunctions. Nevertheless, challenges can be averted if an environmental health education intervention is urgently mounted by the university management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".