Effects of Transition from Remote to In-Person Learning in University Students: A Longitudinal Study
Bibliographic record
Abstract
Previous studies have shown that the transition from the University environment to remote learning impacted student mental health. Our study aimed to investigate the effects of university environment on anxiety and depressive symptoms in health sciences students. Students at the Technical University of Manabí, Ecuador, with 6–10 in-person semesters, who shifted to remote learning and then returned to face-to-face learning were selected. Students responded to the General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). In addition, questions regarding social interaction, physical exercise, mood and sleep habits were also asked. This longitudinal study tracked 323 students during the return to in-person classes and term end. The results showed similar rates of anxiety (GAD-7, p = 0.011-p = 0.002) and depression (PHQ-9 p = 0.001-p = 0.032) among students at week 1 and week 15. Previous diagnosis of depression (OR, 0.171; CI 0.050–0.579, p < 0.005) was shown to correlate with depression levels in week 1, with no changes seen at follow-up. Anxiety levels were shown to be associated with a previous diagnosis of the disorder at week 1, but not at follow-up (OR 0.233; CI 0.085–0.643, p < 0.005). The return to in-person learning among university students maintained levels of anxiety and depressive symptoms, underscoring ongoing vulnerabilities to mental health disorders in this group.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".