Mental health literacy among Afghan adults: A community-based cross-sectional survey study in Herat city
Bibliographic record
Abstract
Background: Health literacy has been defined as the ability to gain access to, understand, and use information in ways which promote and maintain good health. The significance of mental health literacy (MHL)is evolving as a modifiable contributing factor to mental health. This study was undertaken to assess the awareness and attitudes of Afghans on mental disorders. Methods: A cross-sectional survey was administered in August 2022 among Afghans (N=768) living in the Herat province of Afghanistan. The survey examined knowledge and attitude of participants on mental disorders. Results: Generally, most of the participants (99.1%) had poor mental health literacy. 99.4% of participants with an age range of 36-90 years had poor mental health literacy. Almost three-quarter of the participants had poor knowledge of the ability to recognize disorders (72.5%). Less than one-thirds of the participants had good knowledge of where to seek information (29.4%). Conclusion: Mental health literacy rate was found very low among Afghan population. Socio-economic variables found significantly associated with MHL was educational level, economic status, and employment status. Considering the high prevalence of mental disorders in Afghanistan, the government and related non-governmental organizations should implement awareness campaign to increase the knowledge of Afghan people on mental disorders.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".