Mental Health Assessment Among Nursing Students in University of Bohol
Bibliographic record
Abstract
People faced numerous challenges due to the COVID-19 pandemic, and their lives were changed, particularly those of the students. Mental health is a state of well-being in which individuals can cope with the normal adversities in life (WHO, 2004). Good mental health is crucial for students as it could lead to satisfactory academic performance. This study aimed to assess the mental health status of the University of Bohol College of Nursing Students. It delved into the demographic profile and the mental health of the respondents in terms of psychological, physical, and emotional aspects; and looked into the correlation/association between the respondents’ profile and mental health status. It utilized the quantitative, descriptive-correlational research design aided with a modified questionnaire adapted from an article entitled “Here to Help, Body Image, Self-Esteem, and Mental Health” by the Canadian Mental Health Association. Two hundred randomly selected nursing students from the University of Bohol who were enrolled in the 2nd Semester, SY 2021- 2021 were included in the study. Results revealed that respondents have good mental health in terms of psychological, physical, and emotional aspects. When data were subjected to Spearman’s rank test of correlation and chi-square test of association, results revealed that age is significantly correlated to mental health and that the gender and year level has no significant association to mental health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".