Bibliographic record
Abstract
International migration has led to increasingly diverse populations in many high-income countries. With approximately 250,000 newcomers each year, it should be no surprise that developing services that meet the needs of immigrants, refugees, ethnocultural, and racialized populations (IRER) is a major priority in the Mental Health Strategy for Canada. The published Canadian literature on the mental health of diverse populations concludes that differences in the exposure to social risk factors lead to differences in rates of illness for some groups. Whether rates of illness are high or low in a particular group, however, problems in accessing services are ubiquitous. Improving the service response will require political will, leadership, strategic planning, and data, and must include people with lived experience and the populations at highest risk. This paper outlines the “Issues and Options” paper commissioned by the Mental Health Commission of Canada, which used a thorough literature review and a national consultation to develop a model for service development. A health equity approach that utilizes local-population-based planning and the evidence-based interventions that are available for diverse groups could improve services for IRER groups in Canada.
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 | 0.065 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.039 | 0.021 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".