A comparison of walk-in counselling and the wait list model for delivering counselling services
Bibliographic record
Abstract
BACKGROUND: Walk-in counselling has been used to reduce wait times but there are few controlled studies to compare outcomes between walk-in and the traditional model of service delivery. AIMS: To compare change in psychological distress by clients receiving services from two models of service delivery, a walk-in counselling model and a traditional counselling model involving a wait list. METHOD: Mixed-methods sequential explanatory design including quantitative comparison of groups with one pre-test and two follow-ups, and qualitative analysis of interviews with a sub-sample. Five-hundred and twenty-four participants ≥16 years were recruited from two Family Counselling Agencies; the General Health Questionnaire-12 assessed change in psychological distress. RESULTS: Hierarchical linear modelling revealed clients of the walk-in model improved faster and were less distressed at the four-week follow-up compared to the traditional service delivery model. Ten weeks later, both groups had improved and were similar. Participants receiving instrumental services prior to baseline improved more slowly. The qualitative data confirmed participants highly valued the accessibility of the walk-in model, and were frustrated by the lengthy waits associated with the traditional model. CONCLUSIONS: This study improves methodologically on previous studies of walk-in counselling, an approach to service delivery not conducive to randomized controlled trials.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".