Understanding the Differences Between Online and Offline Mental Health Help Seekers: Cross-Sectional Comparative Study
Bibliographic record
Abstract
BACKGROUND: Telepsychiatry has gained considerable attention, particularly during the COVID-19 pandemic. Although various factors influence the choice between online and offline modalities, differences among populations remain underexplored. OBJECTIVE: This study aims to compare adults seeking mental health support online and offline in private clinics. METHODS: In this cross-sectional study, we assessed differences in sociodemographic factors, internet accessibility and usability, previous help-seeking history, personality traits assessed using the Arabic Big Five Personality Inventory, and levels of self-stigma measured using the Self-Stigma of Seeking Help Scale. RESULTS: In total, 259 participants were included (136 online and 123 offline). The online group had a higher proportion of university graduates (P=.02), employed individuals (P<.001), and those with better internet access (P=.03) and higher internet usability (P=.001). The offline group showed higher levels of conscientiousness (P=.003). The primary reasons for choosing online therapy were ease of access and time-saving. Logistic regression identified previous use of online psychiatry as the strongest factor associated with choosing online services (odds ratio [OR] 28.90, 95% CI 11.739-71.165; P<.001). Employment (OR 5.01, 95% CI 1.781-14.080; P=.002), better internet usability (OR 1.69, 95% CI 1.069-2.664; P=.03), and agreeableness (OR 1.16, 95% CI 1.001-1.351; P=.05) were also significant factors. In contrast, previous in-person visits (OR 0.11, 95% CI 0.048-0.269; P<.001), openness (OR 0.85, 95% CI 0.748-0.975; P=.02), and conscientiousness (OR 0.86, 95% CI 0.758-0.971; P=.02) were negatively associated with online preference. CONCLUSIONS: This study highlights key differences between online and offline mental health help seekers, enhances our understanding of treatment modality preferences, and paves the way for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
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".