Bibliographic record
Abstract
Back to table of contents Previous article Next article Letter to the EditorFull AccessPrison-University CollaborationBarbara J. Kane, M.D.Barbara J. Kane, M.D.Published Online:19 Oct 2001https://doi.org/10.1176/pn.36.20.0024bI am writing to applaud the efforts of Dr. Peter Silberfarb and his colleagues at Dartmouth Hitchcock Medical Center in implementing a program to provide psychiatric treatment to prisoners in New Hampshire (Psychiatric News, August 17).My experience of working in our local jail mirrors that of others who have noted much undiagnosed mental illness. Being in a relatively isolated area, however, has allowed me the opportunity to see former prison patients in the psychiatric ward and former hospital patients in the prison. The similarities in their clinical presentation in either setting is often striking and suggests that it is sometimes just the luck of the draw whether police take a mentally ill person to jail or to the hospital.There are differences, of course, but over the years, the differences have become less marked than the similarities. I have seen inmates who, when treated correctly, have yet to re-offend. My experience agrees with a statement in the article that inmates are often extremely appreciative of receiving good care.Regarding security and violence, the jail is a far safer place to work than the hospital as the staff are more easily accessible in an emergency and are not so intimidated by the patients as are nursing staff in a hospital. An added plus is that no one pressures you to discharge a patient in prison prematurely. Patients don't complain to you or negotiate about getting out of prison.I am very encouraged to see the development between Dartmouth and the state of New Hampshire and hope that the initiative spreads as there are many people in the correctional system who can benefit from psychiatric treatment.Prince George, British Columbia ISSUES NewArchived
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.693 | 0.350 |
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".