Prevalence of Depression in Medical Students at the Lebanese University and Exploring its Correlation With Facebook Relevance: A Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of major depression is particularly high in medical students, affecting around one-third of this population. Moreover, online social media, in particular Facebook, is becoming an intrinsic part in the life of a growing proportion of individuals worldwide. OBJECTIVE: Our primary objective is to identify the prevalence of depression in medical students at the Lebanese University Faculty of Medicine, a unique state university in Lebanon, its correlation with the utilization of the interactive features of Facebook, and the way students may resort to these features. METHODS: Students of the Lebanese University Faculty of Medicine were assessed for (1) depression and (2) Facebook activity. To screen for major depression, we used the Patient Health Questionnaire-9 (PHQ-9) scale. To test for Facebook activity, we developed the Facebook Resorting Questionnaire (FbRQ), which measures the degree to which students resort to Facebook. RESULTS: A total of 365 out of 480 students (76.0%) participated in the survey. A total of 25 students were excluded, hence 340 students were included in the final analysis. Current depression was reported in 117 students out of 340 (34.4%) and t tests showed female predominance. Moreover, PHQ-9 score multiple regression analysis showed that feeling depressed is explained 63.5% of the time by specific independent variables studied from the PHQ-9 and the FbRQ. Depression varied significantly among the different academic years (P<.001) and it peaked in the third-year students. One-way analysis of variance (ANOVA) showed that depression and resorting to Facebook had a positive and significant relationship (P=.003) and the different FbRQ categories had significant differences in resorting-to-Facebook power. The like, add friend, and check-in features students used when resorting to Facebook were significantly associated with depression. CONCLUSIONS: This study showed that depression was highly prevalent among students of the Faculty of Medicine at the Lebanese University. Moreover, Facebook may be a promising, helpful, psychological tool for optimizing the management of depression. Our study brought to bear further questions that now prompt further observation and scrutiny to know more about the high rates of depression in this student population, more so in the part of the world studied, and to the growing role of social media.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".