A qualitative examination of psychology graduate students' experiences with guided Internet-delivered cognitive behaviour therapy
Bibliographic record
Abstract
Guided Internet-delivered cognitive behaviour therapy (ICBT) is efficacious for the treatment of a variety of clinical disorders (Spek et al., 2007), yet minimal research has investigated training students in guided ICBT. To contribute to the training literature, through qualitative interviews, this study explored how ICBT was perceived by student therapists (n = 12) trained in guided ICBT. Additionally, facilitators and challenges encountered by students learning guided ICBT were examined. Qualitative analysis revealed that students perceived training to enhance their professional skills in guided ICBT such as how to gain informed consent, address emergencies, and facilitate communication over the Internet. Students described guided ICBT as beneficial for novice therapists learning cognitive behavior therapy as asynchronous communication allowed them to reflect on their clinical emails and seek supervision. Further, students perceived guided ICBT as an important skill for future practice and an avenue to improve patient access to mental health care. Specific facilitators of learning guided ICBT included having access to formal and peer supervision as well as technical assistance, ICBT modules, a functional web application, and detailed policies and procedures for the practice of guided ICBT. Challenges in delivering guided ICBT were also identified by participants such as finding time to learn the approach given other academic commitments, working with non-responsive clients, addressing multiple complex topics over email, and communicating through asynchronous emails. Based on the feedback collected from participants, recommendations for training in guided ICBT are offered along with future research directions.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".