Current and Future Implementation of Digitally Delivered Psychotherapies: An Exploratory Mixed-Methods Investigation of Client, Clinician, and Community Partner Perspectives
Bibliographic record
Abstract
INTRODUCTION: Following the initial outbreak of the COVID-19 pandemic, mental health clinicians rapidly shifted service delivery from in-person to digital. This pivot was instrumental in maintaining continuity of care and meeting increased mental health service demands. Many mental health services have continued to be offered via digital delivery. The long-term implications of delivering services via digital media remain unclear and need to be addressed. OBJECTIVES: This study aimed to identify current micro (i.e., clinician-patient interactions), meso (i.e., clinician-clinic manager interactions), and macro (i.e., government-policy maker interactions) level issues surrounding the use of digital mental health interventions (DMHI). Such integrated assessments are important for optimizing services to improve treatment outcomes and client satisfaction. METHODS: Participants were recruited between January 2022 and April 2023. Quantitative data were collected using a survey informed by the Hexagon Tool. Qualitative data were collected from online semi-structured interviews and focus groups and analyzed using rapid thematic analysis. RESULTS: Survey data were collected from 11 client and 11 clinician participants. Twenty-six community partner participants were interviewed for this study. Client and clinician participants expressed satisfaction with the implementation of DMHI. Community partner participants generally agreed, reporting that such services will play an integral role in mental healthcare moving forward. Community partners shared that certain issues, such as uncertainty surrounding policies and regulations related to digital delivery, must be addressed in the future. CONCLUSIONS: Participants in this study supported the use of DMHI despite difficulties implementing these programs, asserting that such services are not a temporary fix but a pivotal cornerstone in the future of mental healthcare service delivery.
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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".