Barriers to Office-Based Mental Health Care and Interest in E-Communication With Providers: A Survey Study
Bibliographic record
Abstract
BACKGROUND: With rising availability and use of Internet and mobile technology in society, the demand and need for its integration into health care is growing. Despite great potential within mental health care and growing uptake, there is still little evidence to guide how these tools should be integrated into traditional care, and for whom. OBJECTIVE: To examine factors that might inform how e-communication should be implemented in our local outpatient mental health program, including barriers to traditional office-based care, patient preferences, and patient concerns. METHODS: We conducted a survey in the waiting room of our outpatient mental health program located in an urban, academic ambulatory hospital. The survey assessed (1) age, mobile phone ownership, and general e-communication usage, (2) barriers to attending office-based appointments, (3) preferences for, and interest in, e-communication for mental health care, and (4) concerns about e-communication use for mental health care. We analyzed the data descriptively and examined associations between the presence of barriers, identifying as a social media user, and interest level in e-communication. RESULTS: Respondents (N=68) were predominantly in the age range of 25-54 years. The rate of mobile phone ownership was 91% (62/68), and 59% (40/68) of respondents identified as social media users. There was very low existing use of e-communication between providers and patients, with high levels of interest endorsed by survey respondents. Respondents expressed an interest in using e-communication with their provider to share updates and get feedback, coordinate care, and get general information. In regression analysis, both a barrier to care and identifying as a social media user were significantly associated with e-communication interest (P=.03 and P=.003, respectively). E-communication interest was highest among people who both had a barrier to office-based care and were a social media user. Despite high interest, there were also many concerns including privacy and loss of in-person contact. CONCLUSIONS: A high burden of barriers to attending office-based care paired with a high interest in e-communication supports the integration of e-communication within our outpatient services. There may be early adopters to target: those with identified barriers to office-based care and who are active on social media. There is also a need for caution and preservation of existing services for those who choose not to, or cannot, access e-services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".