An Evaluation of the Use of Physical and Chemical Restraints in Geriatric Psychiatric Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Restraints are used in various medical settings to control or restrict problematic patient behavior and can be physical, chemical, or environmental. Restraints can produce harmful psychological and physical effects. OBJECTIVES: The prevalence of restraints in geriatric populations in psychiatric hospital settings in the province of Newfoundland and Labrador (NL) has not yet been documented. METHODS: This retrospective cohort study examined whether any form of restraint was used on patients admitted to the Geriatric Psychiatry Unit (GPU) at the Waterford Hospital in St. John's, NL, from June 1, 2019, to June 1, 2021. FINDINGS: There were 277 admissions to the GPU during the period of observation, and of these, 189 (68.2%) had a chemical restraint administered, 135 (48.7%) had a physical restraint administered, and 123 patients (44.4%) had both a chemical and physical restraint administered. DISCUSSION: Restraints are used to control patient behavior for a number of reasons and in a variety of ways. While this practice is used to promote safer environments for patients, it is not without medical, ethical, and political concerns.This study could promote alternatives to restraints for this geriatric psychiatric population in light of the construction of a new mental health and addictions facility in NL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".