Electronic Mental Health as an Option for Egyptian Psychiatry: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Egypt is a country of nearly 100 million citizens, and there are less than 1000 registered psychiatrists. The mental health care system is under resourced and nearly inaccessible for the majority of the population. In addition, youth under the age of 25 years represent 50% of Egyptian citizens; however, there are no specific services addressing their unique needs. How can the needs of the largest population in the Middle East be effectively addressed? Is a web-based framework an option for Egyptian psychiatrists to serve the population? OBJECTIVE: The aims of this study were to better understand the opinions of psychiatrists on the current state of mental health care services in Egypt and their current knowledge on electronic mental health (EMH); assess the attitudes of Egyptian psychiatrists toward web-based interventions and telemedicine for mental health; and identify perceived advantages and barriers of EMH development in Egypt. METHODS: A cross-sectional survey was conducted online among 640 Egyptian psychiatrists. It included a total of 36 items within a set of 16 questions asking about EMH literacy, integrating EMH into the mental health care system, and the perceived priorities and barriers of EMH. The sampling was supported by Tanta University, a large academic institution close to Cairo. Statistical analysis was performed using SPSS 25 (IBM Corp). Descriptive statistics, the chi-square test, the independent sample t test, and analysis of variance were applied. RESULTS: A total of 188 participants responded (response rate of 29.4%), of which 54.2% (102/188) were female and 54.3% (102/188) were between 30 and 45 years old. Less than half of the participants thought that the current health care system was efficient for adults (69/155, 44.4%), and even less thought it was efficient for youth (44/155, 28.3%). Almost all participants agreed that EMH would be beneficial for patient care (147/155, 94.8%) and that integrating EMH into the current health care system would be a good idea (118/155, 76.2%). The highest rated utility of web-based solutions was documentation, followed by psychoeducation and communication with professionals. The main advantages were to improve access to care in rural areas of the country and its convenience. CONCLUSIONS: There is scarcity of mental health resources in Egypt. Egyptian psychiatrists are interested in EMH and believe web-based platforms can become part of the solution for the Egyptian mental health care system.
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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 | 0.001 |
| 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) | 0.004 | 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".