Bibliographic record
Abstract
Louise Bradley, president and CEO of the Mental Health Commission of Canada, reflects on more than a decade of challenges and opportunities faced by the country's first such commission. She delivered the following speech at the 23rd World Congress of Social Psychiatry on October 24, 2019, in Bucharest, Romania. Using her own lived experience as a springboard for combating stigma and spurring discussion, Bradley is a mental health advocate who has straddled both sides of the care divide. Amplifying the voices of lived experience and caregivers is among her proudest achievements. Through her extensive international exposure, she is convinced that every country is a developing country when it comes to mental health – and this is particularly true when one trains a lens on the mental health outcomes of Indigenous peoples – in Canada and around the world. As a former clinical practitioner and hospital administrator – and a lauded voice for equity and inclusion – Bradley's goal is to challenge her audience to acknowledge their own biases, confront self-stigma, and find our shared humanity
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.053 | 0.030 |
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".