Presentation at the 2010 Provincial Summer Institute: Counseling skills for concurrent
Bibliographic record
Abstract
This presentation was supported by funds from CAMH/CDON and data from NIDA grants no. R01 DA15523, R37-DA11323, R01 DA021174, and CSAT contract no. 270-07-0191. It is available electronically at www.chestnut.org/li/posters. The opinions are those of the authors do not reflect official positions of the government. We would like to thank Christy Scott, Belinda Willlis, Rodney Funk, and Lilia Hristova, Lisa Nicholson, for their assistance in preparing this presentation. Please address comments or questions to the author at mdennis@chestnut.org or 309-451-7801..p 1 The Goals of this Presentation are to: 1. review epidemiological data to illustrate the high rates of concurrent disorders, the rates of recovery across conditions, and their chronic nature 2. summarize the results of recent efforts to increase early identification of concurrent disorders in Ontario 3. examine how the results of recent experiments to improve the ways in which recovery is managed across time and multiple episodes of care
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.193 | 0.019 |
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".