Internet-Based Mental Health Intervention for Depressive Symptoms in Young Adults: Cost-Effectiveness Analysis
Bibliographic record
Abstract
Background: Internet-based cognitive behavioral therapy (CBT) provides psychological interventions to individuals with mild depressive symptoms. Objective: This study aimed to examine the potential cost-effectiveness of internet-based guided-CBT in university students with mild depressive symptoms from the perspective of service providers in Hong Kong. Methods: The outcomes of low-intensity guided internet-based CBT and in-person CBT in a hypothetical cohort of university students with mild depressive symptoms were examined using a 5-year decision-analytic model. Model inputs were obtained from published literature and local data. Model outcomes included direct medical cost, school dropouts, and quality-adjusted life years (QALYs). Sensitivity analyses were conducted on all model parameters. Results: Compared to the in-person group, the internet group gained higher QALYs by 0.0211 QALYs, lowered school dropouts by 0.052%, and saved US $249 in the base-case analysis. In one-way sensitivity analysis, the internet group gained higher QALYs at a lower cost than the in-person group throughout the variation of all model inputs. Probabilistic sensitivity analysis showed that the internet group was cost-effective (at willingness-to-pay threshold was US $48,119/QALY) in 99.7% of the 10,000 Monte Carlo simulations. Conclusions: Internet-based CBT appears to be the cost-effective option when compared to in-person CBT for university students with mild depressive symptoms from the perspective of service providers in Hong Kong.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".