The Relationship between Self-esteem, Anxiety and Depression among UniversityStudents in Lagos, Nigeria
Bibliographic record
Abstract
Previous studies showed negative significant correlations between self-esteem, and emotional states such as anxiety and depression among adolescents especially when studying in a higher institution. This study was, therefore, designed to investigate the prevalence of self-esteem, anxiety and depression, their sociodemographic correlates and the relationships between self-esteem, anxiety, and depression. Methods A cross-sectional design was employed for this study with the participation of 236 students at a university in Lagos, Nigeria. A structured questionnaire was applied to ask about the sociodemographic characteristics of the participants. They were also asked to complete the Rosenberg Self-esteem Scale, and the Hospital Anxiety and Depression Scale (HADS) to determine their levels of self-esteem and probable anxiety and depression and their statistical relationships. Results The findings on the reported levels of self-esteem showed that 22 (9.3%) had low self-esteem and only 12 (5.1%) experienced higher self-esteem. The males had lower self-esteem compared to the female participants. The majority of the participants 154 (65.2%) experienced probable anxiety while about one-third of them 101 (32.8%) manifested with probable depression. There were negative correlations between self-esteem, anxiety and depression -.403 and -.438. Conclusions This study showed that self-esteem negatively correlated with anxiety and depression. This negative association could significantly affect students’ educational achievements and quality of life. There is a need for tertiary institutions to routinely determine the self-esteem of students and also provide psychological interventions aimed at proactively increasing students’ self-esteem to prevent the existence of comorbid psychological and academic distress.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".