Session Title: A Queer Take on A & D Services
Bibliographic record
Abstract
My name is Stacey Boon and I am a counsellor with Addiction Services in Vancouver. I am involved with PRISM in a couple of different capacities. I sit on the evaluation committee for PRISM, and I facilitate a group for queer and transgender women who are early in recovery. I am also a self-identified queer counsellor who is available for one-to-one counselling with clients who prefer a queer counsellor. In his presentation, Devon outlined the consultation process and what specific needs were identified that helped set the direction for PRISM. I will highlight some of the clinical considerations that informed the development of PRISM. I will also provide a snapshot of what PRISM currently looks, clinically speaking. ' References are not imbedded in the slides. This was for aesthetic reasons, and to keep the slides easy to read. A reference list is included in the package for you take away, and there are some great resources if you are interested exploring any of these topics further. For the sake of brevity, inclusiveness, and to avoid repetition, I will use the words, “LGBT2S”, “sexual minorities”, and “queer ” interchangeably. I will preface this discussion by pointing out that it is probably best to conceptualize sexual and gender identity within a cultural framework rather than as a “problem”. As with other theories of cultural
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.482 | 0.104 |
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".