EPA-0784 – Overcoming stigma in mood disorders: a new psychoeducational and behviour modification course
Bibliographic record
Abstract
Mood disorders are very common and associated with significant disability. Stigma because of mental illness is also ubiquitous in the society. We have created a new course for people with mood disorders to help them learn more and be able to practice ways to overcome stigma in their lives and themselves. The course is a closed group with five to eight participants, co-led by a mental health professional and a person with lived experience. The course consists of 7 two hour sessions and focus on the following topics: Introduction and orientation; Depression, Anxiety and Recovery; Self-Stigma; Social Stigma – Family, Friends and Medical settings; Stigma in Education, Housing and the Workplace; Disclosure; and Conclusion. There is a homework assigned between sessions A pilot running of the course has been completed. It was used for a fine-tuning of the course and finalizing the course content. Feedback was encouraged and was used for these purposes The course: ‘Overcoming Stigma in Mood and Anxiety Disorders’ may have a significant role in helping people with those disorders to achieve recovery
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".