Bibliographic record
Abstract
Mental Health affects one in five people in Canada (Statistics Canada, 2022), many of whom will seek treatment at a residential center with support for those recovering from addiction. This paper presents my Graduate Practicum Report completed at an Addictions treatment center, the Top of the World Ranch (TWR), located in the East Kootenays of British Columbia. The practicum was focused on addictions, mental health, and counselling modalities as they apply to the context of addiction, with the goal of applying skills and modalities studied in the course work of my MSW preceding this final practicum. After reviewing mainstream and holistic modalities like Cognitive Behavioural Therapy (CBT), Dialectical Behavioural Therapy (DBT), shinrin yoku, visualization, and meditation, I assembled a strong set of tools to offer value at an addiction treatment center with a focus on trauma informed practice. Key learnings in this report include the usage of art therapy and visualization as effective tools in recovery from addiction. I also found a meaningful way to integrate music into my personal therapeutic approach. Additionally, I found programs that involved visualization and meditation to be both an enjoyable way of working and extraordinarily helpful for clients’ recovery process. My practicum at the TWR was one of the most important learning experiences of my life that will inform all that I do moving forward in my career as a counsellor and social worker.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".