Population-Based Service Planning for Implementation of MBCT: Linking Epidemiologic Data to Practice
Bibliographic record
Abstract
The study explored population-based service planning for mindfulness-based cognitive therapy (MBCT). Evidence suggests the usefulness of MBCT in relapse prevention for individuals reporting three or more major depressive episodes.Depression data were from the Canadian Community Health Survey. A simulation model estimated recurrence rates and population sizes to sustain MBCT therapists (each conducting two ten-person groups per year).Approximately 4.2% of the population are candidates for MBCT, and about 13 candidates would arise annually per 10,000 population. If MBCT was acceptable to 20%, for example, a population of 200,000 could support two therapists.A large proportion of the population is eligible for MBCT introduction; however, after introduction, the rate of emergence of candidates would yield a smaller patient pool, which may limit implementation in small population centers. Treatment acceptability is a key variable. These analyses highlight the potential value of epidemiologic data and simulation modeling in planning.
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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 | 0.032 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".