Public Attitudes Toward Guided Internet-Based Therapies: Web-Based Survey Study
Bibliographic record
Abstract
Background Internet interventions have been proposed to improve the accessibility and use of evidence-based psychological treatments. However, little is known about attitudes toward such treatments, which can be an important barrier to their use. Objective This study aimed to (1) determine attitudes toward guided internet interventions, (2) assess its acceptability compared with other internet-based formats, and (3) explore predictors of acceptance. Methods A convenience-sample Web-based survey (N=646) assessed attitudes toward guided internet therapies (ie, perceived usefulness and helpfulness, and advantage relative to face-to-face therapy), preferences for delivery modes (ie, e-preference: guided internet interventions, unguided internet interventions, or videoconferencing psychotherapy), and potential predictors of attitudes and preferences: sociodemographics, help-seeking–related variables, attachment style, and perceived stress. Results Although most participants perceived internet interventions as useful or helpful (426/646, 65.9%), a few indicated their advantage relative to face-to-face therapy (56/646, 8.7%). Most participants preferred guided internet interventions (252/646, 39.0%) over videoconferencing psychotherapy (147/646, 22.8%), unguided internet interventions (124/646, 19.2%), and not using internet interventions (121/646, 18.8%; missing data: 1/646, 0.2%). Attachment avoidance and stress were related to e-preference (all P<.05). Moreover, preference for therapist-guided internet interventions was higher for individuals who were aware of internet-based treatment (χ26=12.8; P=.046). Conclusions Participants assessed therapist-guided internet interventions as helpful, but not equivalent to face-to-face therapies. The vast majority (523/646, 81.0%) of the participants were potentially willing to use internet-based approaches. In lieu of providing patients with only one specific low-intensity treatment, implementation concepts should offer several options, including guided internet interventions, but not limited to them. Conversely, our results also indicate that efforts should focus on increasing public knowledge about internet interventions, including information about their effectiveness, to promote acceptance and uptake.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.003 | 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".