Conventional Cognitive Behavioral Therapy Facilitated by an Internet-Based Support System: Feasibility Study at a Psychiatric Outpatient Clinic
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioral therapies have been shown to be effective for a variety of psychiatric and somatic disorders, but some obstacles can be noted in regular psychiatric care; for example, low adherence to treatment protocols may undermine effects. Treatments delivered via the Internet have shown promising results, and it is an open question if the blend of Internet-delivered and conventional face-to-face cognitive behavioral therapies may help to overcome some of the barriers of evidence-based treatments in psychiatric care. OBJECTIVE: We evaluated the feasibility of an Internet-based support system at an outpatient psychiatric clinic in Sweden. For instance, the support system made it possible to send messages and share information between the therapist and the patient before and after therapy sessions at the clinic. METHODS: Nine clinical psychologists participated and 33 patients were enrolled in the current study. We evaluated the usability and technology acceptance after 12 weeks of access. Moreover, clinical data on common psychiatric symptoms were assessed before and after the presentation of the support system. RESULTS: In line with our previous study in a university setting, the Internet-based support system has the potential to be feasible also when delivered in a regular psychiatric setting. Notably, some components in the system were less frequently used. We also found that patients improved on common outcome measures for depressive and anxious symptoms (effect sizes, as determined by Cohen d, ranged from 0.20-0.69). CONCLUSIONS: This study adds to the literature suggesting that modern information technology could be aligned with conventional face-to-face services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".