Predictors of Mental Health Literacy in a Sample of Health Care Major Students: Pilot Evaluation Study
Bibliographic record
Abstract
BACKGROUND: The numerous mental health awareness campaigns during the COVID-19 pandemic have shifted our understanding and perception of mental health. OBJECTIVE: The purpose of this study is to evaluate predictors of mental health literacy (MHL), that is, one's knowledge and beliefs about mental disorders. We evaluate whether digital health literacy, empathy, and mentalizing contribute to MHL. METHODS: Our sample consisted of 89 health care major students, aged between 17 and 32 years, studying at a university in Lebanon. The Mental Health Literacy Scale for Healthcare Students (MHLS-HS), the eHealth Literacy Questionnaire (eHLQ), the Basic Empathy Scale (BES), and the Reflective Functioning Questionnaire-8 (RFQ-8) were used. RESULTS: Multiple regression analyses revealed that the Engagement in Own Health subscale of digital health literacy constituted a predictor of MHL. While empathy and mentalizing did not directly predict MHL, they were found to predict components of MHL. CONCLUSIONS: This is the first study to evaluate digital health literacy, empathy, and mentalizing as predictors of MHL in Lebanon, a country where mental health is still considered taboo. Moreover, this pilot study is the first to provide some support for the predictive role of some digital health literacy subscales on MHL in light of the rise of the digital era following the COVID-19 pandemic.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".