Seclusion and mechanical restraint in the wake of the COVID-19 pandemic: an increased use in mental health settings
Bibliographic record
Abstract
Purpose: COVID-19 pandemic-related restrictions have significantly changed the environment and the delivery of direct care in all health services, including psychiatric hospitals. The aim of the study is two-fold: 1) to retrospectively assess the incidence of seclusion and mechanical restraint in a Quebec mental health hospital over 4 years; and 2) to assess the impact of the COVID-19 pandemic on their incidence. Methods: We conducted a retrospective study based on medical records from a Quebec mental health hospital collected (a) from April 2016 to March 2019), (b) from April 2019 to March 2020 (pre-COVID onset), and (c) from April 2020 to March 2021 (post-COVID onset). Descriptive statistics, chi square tests, Mann-Kendall test and Welch's t-test were performed. Results: Following several years during which the use of restrictive measures remained stable, we have noted a significant increase within the first year following the COVID-19 pandemic. This increase can be seen in the use of both seclusion and restraints, which have risen 205% and 170% respectively. Conclusion: There are a multitude of factors associated with the incidence of seclusion and restraint that have the potential to be triggered during emergencies and global crisis situations, impacting in turn the rights of an already vulnerable population.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".