Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID‐19
Bibliographic record
Abstract
WHAT IS KNOWN ABOUT THE SUBJECT?: In a survey conducted by the World Health Organization (WHO) in the summer of 2020, 93% of countries worldwide acknowledged negative impacts on their mental health services. Previous research during the H1N1 pandemic in 2009 established an increase of patient aggression in psychiatric facilities. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: Despite expected worsening of mental health, our hospital observed reductions in aggressive behaviour among inpatients and subsequent use of coercive interventions by staff in the months following Covid-19 pandemic restrictions being implemented. The downward trend in incidents observed during the pandemic has suggested that aggression in mental health hospitals may be more situation-specific and less so a factor of mental illness. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: We believe that the reduction in aggressive behaviour observed during the pandemic is related to changes in our organization that occurred in response to concerns about patient well-being; our co-design approach shifted trust, choice and power. Therefore, practices that support these constructs are needed to maintain the outcomes we experienced. Rather than return to normal in the wake of the pandemic, we are strongly encouraged to sustain the changes we made and continue to find better ways to support and work with the individuals who rely on or use our services. ABSTRACT: The global COVID-19 pandemic has dramatically changed the operation of health care such that many services were put on hold as patients were triaged differently, people delayed seeking care, and transition to virtual care was enacted, including in psychiatric facilities. Most of the media dialogue has been negative; however, there have been some silver linings observed. Coinciding with the pandemic has been a reduction in aggressive incidents at our psychiatric hospital, along with the decreased need to use restraints and seclusion to manage behaviour. In this paper, we are taking stock of the changes that have occurred in response to the pandemic in an attempt to share our learnings and offer suggestions so that health care does not necessarily return to "normal".
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".