Psychotherapy Access Barriers and Interest in Digital Mental Health Interventions Among Adults With Treatment Needs: Survey Study
Bibliographic record
Abstract
BACKGROUND: Digital mental health interventions (DMHIs) are a promising approach to reducing the public health burden of mental illness. DMHIs are efficacious, can provide evidence-based treatment with few resources, and are highly scalable relative to one-on-one face-to-face psychotherapy. There is potential for DMHIs to substantially reduce unmet treatment needs by circumventing structural barriers to treatment access (eg, cost, geography, and time). However, epidemiological research on perceived barriers to mental health care use demonstrates that attitudinal barriers, such as the lack of perceived need for treatment, are the most common self-reported reasons for not accessing care. Thus, the most important barriers to accessing traditional psychotherapy may also be barriers to accessing DMHIs. OBJECTIVE: This study aimed to explore whether attitudinal barriers to traditional psychotherapy access might also serve as barriers to DMHI uptake. We explored the relationships between individuals' structural versus attitudinal barriers to accessing psychotherapy and their indicators of potential use of internet-delivered guided self-help (GSH). METHODS: We collected survey data from 971 US adults who were recruited online via Prolific and screened for the presence of psychological distress. Participants provided information about demographic characteristics, current symptoms, and the use of psychotherapy in the past year. Those without past-year psychotherapy use (640/971, 65.9%) answered questions about perceived barriers to psychotherapy access, selecting all contributing barriers to not using psychotherapy and a primary barrier. Participants also read detailed information about a GSH intervention. Primary outcomes were participants' self-reported interest in the GSH intervention and self-reported likelihood of using the intervention if offered to them. RESULTS: Individuals who had used psychotherapy in the past year reported greater interest in GSH than those who had not (odds ratio [OR] 2.38, 95% CI 1.86-3.06; P<.001) and greater self-reported likelihood of using GSH (OR 2.25, 95% CI 1.71-2.96; P<.001). Attitudinal primary barriers (eg, lack of perceived need; 336/640, 52.5%) were more common than structural primary barriers (eg, money or insurance; 244/640, 38.1%). Relative to endorsing a structural primary barrier, endorsing an attitudinal primary barrier was associated with lower interest in GSH (OR 0.44, 95% CI 0.32-0.6; across all 3 barrier types, P<.001) and lower self-reported likelihood of using GSH (OR 0.61, 95% CI 0.43-0.87; P=.045). We found no statistically significant differences in primary study outcomes by race or ethnicity or by income, but income had a statistically significant relationship with primary barrier type (ORs 0.27-3.71; P=.045). CONCLUSIONS: Our findings suggest that attitudinal barriers to traditional psychotherapy use may also serve as barriers to DMHI use, suggesting that disregarding the role of attitudinal barriers may limit the reach of DMHIs. Future research should seek to further understand the relationship between general treatment-seeking attitudes and attitudes about DMHIs to inform the design and marketing of DMHIs.
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 | 0.002 |
| 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) | 0.002 | 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".