Leveraging Linked Ontario’s Health-administrative Data to Evaluate Internet-delivered Cognitive Behavioural Therapy (iCBT) in routine care
Bibliographic record
Abstract
ObjectiveVirtual interventions show promise for meeting growing mental health (MH) service demands. In 2020, Ontario launched Internet-delivered Cognitive Behavioural Therapy (iCBT), an evidence-based, self-led, asynchronous intervention for anxiety and depression. While its efficacy has been demonstrated in randomized controlled trials, we examine its real-world reach and treatment completion in routine care. ApproachLinking iCBT records with health-administrative databases, we examined the characteristics (clinical, sociodemographic, health service use) of individuals receiving iCBT (via a pilot program), compared to fully-synchronous CBT (standard of care), ascertained who is more likely to receive either modality, and determined factors associated with treatment completion using Logistic regressions adjusted for sociodemographics, baseline depression (PHQ-9) and anxiety (GAD-7), and outpatient and acute MH-related service use one year prior to enrollment. ResultsAmong N=167 individuals receiving iCBT at the Centre for Addiction and Mental Health (Jan 2020-Aug 2021) and N=300 controls receiving fully-synchronous CBT via Ontario’s Structured Psychotherapy Program, we found that older individuals, those with lower baseline anxiety, and those with greater prior MH service use were more likely to receive iCBT. Intervention modality was the only significant predictor for treatment completion, with 51% lesser odds among iCBT clients (ORadjusted=0.49, 95%CI 0.31-0.76). ConclusionsWhile certain demographic characteristics differentiate iCBT recipients from those receiving standard CBT, treatment adherence is lower in self-led, asynchronous therapy. ImplicationsThis study examines iCBT in routine care and informs its wider implementation so it can reach the target population, ease access burdens, and improve MH service delivery. Future work will examine treatment effectiveness.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".