Report on the Distribution of the Social Determinants of Health and Health Equity in a Forensic Psychiatry Program
Bibliographic record
Abstract
The social determinants of health are important factors that shape a person’s well-being, life expectancy, and quality of life. The environments in which people live, work, and play are paramount in determining their overall health. As such, viewing health as an outcome, not only of individual choices and biomedical factors but also of socioenvironmental influences, can be an important lens to guide health-care practice. This report examined the social determinants of health of people admitted to inpatient units in a forensic psychiatry program in a major Canadian urban centre. Twenty health variables were collected from the Resident Assessment Instrument–Mental Health form. A deprivation scale was created to understand social and material inequality on a gradient. Findings showed that those surveyed had high rates of poor social determinant of health factors, such as low educational attainment, insecure housing, and lack of secure employment before their admission to the program. Chi-square tests showed associations between material deprivation, race, and comorbidity status. The findings may influence a multisectorial approach to mental illness prevention, management, and recovery practices.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".