Public views on e-mental health services –a systematic review of the current evidence
Bibliographic record
Abstract
Background: Considering both the Internet becoming a common mental health advisor and the unmet needs among various European mental health populations due to limited care resources, lacking health literacy or stigmatised beliefs, e-mental health services are suggested as appropriate option to improve the access to professional help. To overcome such barriers, however, knowledge about users´ preferences predicting the acceptability and perceived helpfulness of online self-help is required. Therefore, this review aims to determine the evidence on attitudes toward e-mental-health in the general population. Methods: A systematic search through Medline, PsycINFO and Cochrane Library was carried out, including research papers published in peer-reviewed journals between 2010 and 2015. Inclusion criteria contained studies focussing on preferences and attitudes toward e-mental health among adults. Clinical trials or surveys on the views of providers, participants in an intervention (e.g. clients) or specific risk groups were excluded. Findings: From the 63 results identified in electronic databases, four papers met the inclusion criteria. Sample sizes ranged from N=217 to 2.411. Data mainly stem from Canada, Australia and Austria, respectively Germany. Methodology varied across the studies. Overall, results indicated type-specific differences for preferences. Despite the low likelihood of e-mental health use in the future observed in most samples, health literacy and e-awareness tended to be associated with positive attitudes. Conclusions: Currently, the evidence on users´ preferences and attitudes toward e-mental health services remains scarce. Due to the limited research available, and methodological issues, further research is recommended in order to enable informed decisions.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".