Mental Health Literacy and Associated Factors among Secondary School Students in Bhaktapur, Nepal
Bibliographic record
Abstract
Introduction: Mental health problems like depression and anxiety are the leading contributors to the global burden of disease. Mental health problems are common in adults and children in Nepal, accounting for 13.2% and 11.2% of the population while only 21% sought treatment. Evaluation of mental health literacy is important in assisting the development of intervention and policies toward preventing mental health problems. This study aimed to assess the mental health literacy among the secondary school students of Bhaktapur municipality and identify the factors associated with it. Methods: A cross-sectional descriptive study was conducted in December 2019 among 468 students of grade11 and 12. We selected the study sample using two-stage cluster sampling technique. A self-administered questionnaire was used for the data collection using a modified mental health literacy scale. Collected data were entered in EpiData 3.1 and SPSS 17.0. Descriptive analysis was done to find out the level of MHL. Variables that were found statistically significant (p<0.05) in the univariate analysis were further analysed using multiple linear regression method. Ethical approval was taken from the Institutional Review Committee of the Institute of Medicine, Nepal. Results: The participants exhibited moderate level of mental health literacy score of 110.9 8 (SD=±11.11).This study shows that age below 18 years (β= 2.13, 95% CI= 0.093to4.164), science faculty (β= 6. 41, 95% CI= 3.71to8.57), internet source for health information (β=2.31, 95% CI= 0.21to4.41), part-time job (β= -6.78, 95% CI= -9.30to -4.25) and mental distress (β= -3.37, 95% CI= -5.27to -1.47) were significantly associated with MHL in the students. Conclusions: Awareness of existing MHL levels in the secondary school students is crucial for the evaluation of targeted educational interventions and for the further development and implementation of these interventions in the future. This study also emphasizes the need for school mental health program and to include mental health literacy in the school curriculum. Keywords: Mental health literacy, Health literacy, School students
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 | 0.000 | 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) | 0.003 | 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".