Patient Satisfaction and Recommendations for Delivering a Group-Based Intensive Outpatient Program via Telemental Health During the COVID-19 Pandemic: Cross-sectional Cohort Study
Bibliographic record
Abstract
BACKGROUND: Although group-based intensive outpatient programs (IOPs) are a level of care commonly utilized by adults with serious mental illness, few studies have examined the acceptability of group-based IOPs that required rapid transition to a telemental health (TMH) format during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to evaluate patient satisfaction and future recommendations for a group-based IOP that was transitioned to a TMH format during the COVID-19 pandemic. METHODS: A 17-item patient satisfaction questionnaire was completed by patients at discharge and covered 3 areas: IOP TMH satisfaction, future recommendations, and video technology challenges. Descriptive and content analyses were conducted for the quantitative and open-ended questions, respectively. RESULTS: A total of 76 patients completed the program in 2020. A subset of patients (n=40, 53%) responded to the survey at program discharge. The results indicated that the patients were satisfied overall with the TMH program format; 50% (n=20) of the patients preferred the program continue offering the TMH format, and the rest preferred returning to in-person formats after the pandemic. The patients indicated the elements of the program that they found most valuable and provided recommendations for future program improvement. CONCLUSIONS: Overall, adults with serious mental illness reported high satisfaction with the group-based IOP delivered via TMH. Health care systems may want to consider offering both TMH and in-person formats regardless of the state of the pandemic. Patients' feedback on future improvements should be considered to help ensure long-term success.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 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".