Educating Staff and Volunteers at a Therapeutic Community for Homeless Persons with Co-Morbid Disorders: Support for Therapeutic Community Curriculum
Bibliographic record
Abstract
In Autumn 2011, 12 participants at a newly-formed therapeutic community in western Canada completed 54 hours of Therapeutic Community Curriculum (TCC). Participants completed a shortened version of the Survey of Essential Elements Questionnaire (SEEQ) before and after the training (De Leon & Melnick, 1993b). A paired sample t-test of the SEEQ items revealed a moderate-strong effect size (Cohen's d = .68) and the positive effect of 11 items, at least 1 each from the 6 conceptual domains of the SEEQ. Following training, a focus group found that participants had a better understanding of TC theory and the concepts and believed training would help them in their roles in the community. However, some volunteers felt overwhelmed by the amount of detail covered during training and both staff and volunteers suggested that the experiential exercises could be enhanced.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".