Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent research on approaches to improving social inclusion for people with mental disabilities is reviewed. RECENT FINDINGS: We describe four approaches (or tools) that can be used to improve social inclusion for people with mental disabilities: legislation, community-based supports and services, antistigma/antidiscrimination initiatives, and system monitoring and evaluation. While legislative solutions are the most prevalent, and provide an important framework to support social inclusion, research shows that their full implementation remains problematic. Community-based supports and services that are person-centered and recovery-oriented hold considerable promise, but they are not widely available nor have they been widely evaluated. Antistigma and antidiscrimination strategies are gaining in popularity and offer important avenues for eliminating social barriers and promoting adequate and equitable access to care. Finally, in the context of the current human rights and evidence-based health paradigms, systematic evidence will be needed to support efforts to promote social inclusion for people with mental disabilities, highlight social inequities, and develop best practice approaches. SUMMARY: Tools that promote social inclusion of persons with mental disabilities are available, though not yet implemented in a way to fully realize the goals of current disability discourse.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".