Patient and Therapist Perceptions of a Publicly Funded Internet-Based Cognitive Behavioral Therapy (iCBT) Program for Ontario Adults During the COVID-19 Pandemic: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: To address the anticipated rise in mental health symptoms experienced at the population level during the COVID-19 pandemic, the Ontario government provided 2 therapist-assisted internet-delivered cognitive behavioral therapy (iCBT) programs to adults free of charge at the point of service. OBJECTIVE: The study aims to explore the facilitators of and barriers to implementing iCBT at the population level in Ontario, Canada, from the perspective of patients and therapists to better understand how therapist-assisted iCBT programs can be effectively implemented at the population level and inform strategies for enhancing service delivery and integration into the health care system. METHODS: Using a convenience sampling methodology, semistructured interviews were conducted with 10 therapists who delivered iCBT and 20 patients who received iCBT through either of the publicly funded programs to explore their perspectives of the program. Interview data were analyzed using inductive thematic analysis to generate themes. RESULTS: Six salient themes were identified. Facilitators included the therapist-assisted nature of the program; the ease of registration and the lack of cost; and the feasibility of completing the psychoeducational modules given the online and self-paced nature of the program. Barriers included challenges with the online remote modality for developing the therapeutic alliance; the program's generalized nature, which limited customization to individual needs; and a lack of formal integration between the iCBT program and the health care system. CONCLUSIONS: Although the program was generally well-received by patients and therapists due to its accessibility and feasibility, the digital format of the program presented both benefits and unique challenges. Strategies for improving the quality of service delivery include opportunities for synchronous communication between therapists and patients, options for increased customization, and the formal integration of iCBT into a broader stepped-care model that centralizes patient referrals between care providers and promotes continuity of care.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".