Bibliographic record
Abstract
The article, Seclusion Practice in a Canadian Foren sic Hospital, by A. G. Ahmed and M. Lepnurm1 evokes further questions about the use of seclusion and restraint in psychiatric hospitals. This subject remains at the heart of intense debatewithin psychi atric and consumergroups, and recently there have been legislative interventions that have intensified the discussion. Although most of the debate and re search have focused on the civil psychiatric hospital, there is comparatively little information available about the practiceofseclusion and restraint in foren sichospitals or in those facilities that provide psychi atric care to prison inmates, as in this example. Guttheil and Applebaum2 provide a brief review ofsome of thebenefits ofseclusion, which they com pare witha prescription ofspace, that may behelpful in providing external controls to disturbed patients who have poor internal controls. They note a num ber of potential advantages, including containment for the out-of-control patient, isolation from dis tressing interpersonal relationships, and diminished sensory input. They caution, however, that seclusion and/or restraint can beeasily misused, in particular if it serves a punitive function, is a substitute for staff time or attention, or is a mechanism for the acting
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.068 | 0.043 |
| Insufficient payload (model declined to judge) | 0.012 | 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".