Introducing internet-based cognitive behavioral therapy in the Latvian government-funded mental health sector
Bibliographic record
Abstract
As mental health challenges grow globally, innovative interventions are being sought. Internet-based Cognitive Behavioral Therapy (iCBT) offers a promising alternative to traditional psychotherapy-reducing costs, improving accessibility, and addressing healthcare worker shortages in the public sector-essential for Latvia, where many people live in rural areas, have limited income and there is a lack of mental health specialists, making it difficult for patients to access psychological support. In 2024, Latvia launched its first government-funded iCBT pilot. This study introduces the framework and implementation strategy of the Latvian iCBT pilot, done in collaboration with Finland's HUS and the HealthFox platform. The program targets young adults from the age of 18 to 25 with mild to moderate depression and anxiety, based on validated clinical thresholds (PHQ-9 >8, GAD-7 >10). The population clinical symptoms were designed similarly to previous experience with iCBT evaluated in Finland. The structured therapy, delivered through a mobile app, includes weekly guided sessions, personalized therapist feedback, and interactive digital modules. This article examines the architecture of the pilot-its referral system, therapy modules, data collection process, and therapist responsibilities. Also, it is looking at it within broader global evidence on iCBT efficacy, dropout rates, and patient satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".