Impacts of CoVID-19 Pandemic on the Psychological Well Being of Students in a Nigerian University
Bibliographic record
Abstract
The novel Covid-19 pandemic has caused disruption of several activities globally. It has resulted in lockdown of economic activities in most countries including Nigeria. The effects of the pandemic and the associated lockdown on the mental health status of Nigerian students have not taken into perspective in the control efforts. This study was conducted to assess the burden of the psychological problems associated with COVID-19 pandemic and coping strategies among students at Bowen University, Iwo (BUI), Nigeria. A cross-sectional survey was conducted, and multi-stage sampling technique employed to recruit 433 eligible respondents. Validated online self-administered, semi-structured questionnaire was used to collect relevant information from respondents. Appropriate summary statistics were carried out, and Binary logistic regression model was developed to examine protective/risk-factors of decreased mental wellbeing among the respondents. Mean age of the respondents was 20.11 ± 2.9 years, almost three-quarter (72.7%) of them were females. In all, 55.0% of the respondents had decreased psychological wellbeing because of the lockdown. Protective factors against decreased mental wellbeing included online catting with friends/watching films (OR=0.22, 95%CI=1.37-3.59), and participating in online vocational training (OR=0.25, 95%CI=0.25-0.61). The odds of decreased mental wellbeing was significantly higher in students who indicated a need for substance use as coping strategy(OR=1.50, 95%CI=0.55-4.05), and those who were not satisfied with online teaching method (OR=5.34, 95%CI=4.13-9.18).The mental health impacts of COVID-19 on the Nigerian students is huge. Thus, psychological wellbeing of the Nigerian students should be taken into cognizance and prioritized in the post-COVID-19 national rebuilding strategies. Keywords: Covid-19, Determinants, Depression, Mental wellbeing, Mental disorders, Suicide
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".