Investigating the Impact of COVID-19 Lockdown on the Psychological Health of University Students and Their Attitudes Toward Mobile Mental Health Solutions: Two-Part Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 outbreak was first reported to the World Health Organization on December 31, 2019, and it was officially declared a public health emergency of international concern on January 30, 2020. The COVID-19 outbreak and the safety measures taken to control it caused many psychological issues in populations worldwide, such as depression, anxiety, and stress. OBJECTIVE: The objectives of this study were to assess the psychological effects of the lockdown due to the COVID-19 outbreak on university students in the United Arab Emirates (UAE) and to investigate the students' awareness of mobile mental health care apps as well as their attitudes toward the use of these apps. METHODS: A two-part self-administered web-based questionnaire was delivered to students at United Arab Emirates University. The first part of the questionnaire assessed the mental state of the participants using the 12-item General Health Questionnaire (GHQ-12), while the second part contained questions investigating the participants' awareness of and attitudes toward mental health care apps. Students were invited to fill out the web-based questionnaire via social media and mailing lists. RESULTS: A total of 154 students participated in the survey, and the majority were female. The results of the GHQ-12 analysis showed that the students were experiencing psychological issues related to depression and anxiety as well as social dysfunction. The results also revealed a lack of awareness of mental health care apps and uncertainty regarding the use of such apps. Approximately one-third of the participants (44/154, 28.6%) suggested preferred functionalities and characteristics of mobile mental health care apps, such as affordable price, simple design, ease of use, web-based therapy, communication with others experiencing the same issues, and tracking of mental status. CONCLUSIONS: Like many groups of people worldwide, university students in the UAE were psychologically affected by the lockdown due to the COVID-19 outbreak. Although apps can be useful tools for mental health care delivery, especially in circumstances such as those produced by the outbreak, the students in this study showed a lack of awareness of these apps and mixed attitudes toward them. Improving the digital health literacy of university students in the UAE by increasing their awareness of mental health care apps and the treatment methods and benefits of the apps, as well as involving students in the app creation process, may encourage students to use these tools for mental health care.
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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) | 0.001 | 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) | 0.003 | 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".