Cost-effectiveness analysis of single-session walk-in counselling
Bibliographic record
Abstract
BACKGROUND: An increasing number of family service agencies and community-based mental health service providers are implementing a single-session walk-in counselling (SSWIC) as an alternative to traditional counselling. However, few economic evaluations have been undertaken. AIMS: To conduct a cost-effectiveness analysis of two models of service delivery, SSWIC compared to being waitlisted for traditional counselling. METHODS: A quasi-experimental design was employed. Data were collected from two community-based Family Service Agencies, one using SSWIC and one using traditional counselling. Participants were assessed at baseline and four weeks after the baseline. Cost-effectiveness was estimated from the societal and payer's perspective. RESULTS: The societal and payer's costs for SSWIC were higher than for those waiting for traditional counselling, and health outcomes were better. SSWIC is not cost-effective compared to being on the waitlist for traditional counselling (or, for a few patients, having received counselling, but after a wait of several weeks). CONCLUSIONS: SSWIC has the potential to reduce the pressure on the mental health care system by reducing emergency visits and wait lists for ongoing mental health services and eliminating costly-no shows at counselling appointments. Long-term studies involving multiple walk-in counselling services and comparison services are needed to support the findings of this study.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".