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Record W4416015509 · doi:10.2196/69305

Understanding the Differences Between Online and Offline Mental Health Help Seekers: Cross-Sectional Comparative Study

2025· article· en· W4416015509 on OpenAlexvenueno aff
Mohamed Adwi, Basma Khalaf Mahmoud, Noha Amer, Roa Gamal Alamrawy, Ismail Sadek, Mohamed Mohamed Ali Elsheikh

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthOnline and offlineKey (lock)Modality (human–computer interaction)mHealthPublic health

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.368
GPT teacher head0.517
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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