Mental illness, substance dependence, And Suicidality: Secondary Data Analysis
Bibliographic record
Abstract
Abstract Mental illness, substance dependence, and suicide are three of the most prevalent social and public health concerns throughout North America. Recent estimates indicate that every 20 minutes somewhere in the United States or Canada a suicide occurs. In the United States alone there are 30,000 suicides per year (Bush, Fawcett,, Jacobs, 2003; Institute of Medicine, 2002). This chapter seeks to explain the contributing factors leading to rehospitalization and suicide risk among the psychiatric, substance abuse, and substance dependent populations. Data from a university medical center will be reviewed regarding characteristics of inpatient hospitalization and potential contributing factors increasing the individual’s risk of a suicide attempt following inpatient psychiatric/ substance dependence hospitalization. In 2001 approximately 15 million adults age 18 or greater were estimated to have suffered from a severe mental illness during the previous year. The population with the highest rate of severe mental illness was the 18-to-25-year-old age group (12 %). Persons ages 26-49 demonstrated a rate of approximately 8%, with the 50 or older age group demonstrating a rate of 5%. Females were more likely than males to have a diagnosis consistent with severe mental illness (9% female versus 6% male) (National Institute of Mental Health, 2001; Bush et al., 2003; Institute of Medicine, 2002).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".