The case for single-session therapy: Does the empirical evidence support the increased prevalence of this service delivery model?
Bibliographic record
Abstract
BACKGROUND: A significant increase in the number of walk-in counselling clinics offering single-session therapy (SST) prompted this review of the empirical support for the effectiveness of SST. AIMS: The article is intended to (1) increase practitioners' knowledge of the empirical support for the effectiveness of single-session counselling with client populations typically served in community-based mental health and counselling agencies and (2) identify priorities for future research on SST. METHOD: A thorough review of relevant databases was undertaken to locate published studies reporting client outcomes following SST. The focus of the review is research involving clients and presenting problems typically seen in community-based mental health and family counselling agencies. RESULTS: The findings suggest that the majority of clients attending either previously scheduled or walk-in SST find it sufficient and helpful. The studies imply that this model of service delivery leads to perceived improvement in presenting problems in general, and on specific measures of variables such as depression, anxiety, distress level and confidence in parenting skills. CONCLUSIONS: Many of the studies have methodological limitations, and future research requires increased use of standardized measures, control groups and larger and more diverse samples.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".