Health Care Professionals' Engagement With Digital Mental Health Interventions in the United Kingdom and China: Mixed Methods Study on Engagement Factors and Design Implications
Bibliographic record
Abstract
Background: Mental health issues like occupational stress and burnout, compounded with the after-effects of COVID-19, have affected health care professionals (HCPs) around the world. Digital mental health interventions (DMHIs) can be accessible and effective in supporting well-being among HCPs. However, low engagement rates of DMHIs are frequently reported, limiting the potential effectiveness. More evidence is needed to reveal the factors that impact HCPs' decision to adopt and engage with DMHIs. Objective: This study aims to explore HCPs' motivation to engage with DMHIs and identify key factors affecting their engagement. Amongst these, we include cultural factors impacting DMHI perception and engagement among HCPs. Methods: We used a mixed method approach, with a cross-sectional survey (n=438) and semistructured interviews (n=25) with HCPs from the United Kingdom and China. Participants were recruited from one major public hospital in each country. Results: Our results demonstrated a generally low engagement rate with DMHIs among HCPs from the 2 countries. Several key factors that affect DMHI engagement were identified, including belonging to underrepresented cultural and ethnic groups, limited mental health knowledge, low perceived need, lack of time, needs for relevance and personal-based support, and cultural elements like self-stigma. The results support recommendations for DMHIs for HCPs. Conclusions: Although DMHIs can be an ideal alternative mental health support for HCPs, engagement rates among HCPs in China and the United Kingdom are still low due to multiple factors and barriers. More research is needed to develop and evaluate tailored DMHIs with unique designs and content that HCPs can engage from various cultural backgrounds.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".