Dissociation in Long-Term Care Home Staff During COVID-19: Challenges and Promising Practices
Bibliographic record
Abstract
OBJECTIVES: Long-term care (LTC) staff may develop dissociation due to high-stress work environments and trauma exposures. This study aimed to (1) assess the prevalence of pathological dissociation in LTC home staff during the COVID-19 pandemic; (2) examine the associations of pathological dissociation with demographic characteristics, mental health, insomnia, and professional quality of life; and (3) examine whether pathological dissociation was sensitive to change following a coherent breathing intervention. DESIGN: We analyzed data from a pre-post breathing intervention study conducted between January and September 2022. SETTINGS AND PARTICIPANTS: Participants were 254 staff (care aides, nurses, and managers) from 31 LTC homes in Alberta, Canada. METHODS: test and t tests to examine the association of pathological dissociation with other variables pre-intervention. We used a 2-level random intercept logistic regression analysis to examine the change in pathological dissociation from pre- to post-intervention. RESULTS: About 12% and 8% of the sample experienced pathological dissociation pre- and post-intervention, respectively. Pathological dissociation was significantly associated with stress, psychological distress, anxiety, depression, posttraumatic stress disorder, and insomnia (P < .05); it was also significantly associated with language, race, and professional role (P < .05). Participants had lower odds of experiencing pathological dissociation post-intervention compared with pre-intervention (odds ratio, 0.41; P = .045). CONCLUSIONS AND IMPLICATIONS: LTC home staff exhibited a high prevalence of pathological dissociation during COVID-19, significantly linked to other mental health measures. A coherent breathing intervention showed potential in reducing reports of dissociation. Further research is needed to understand dissociation in LTC staff and its interplay with mental health outcomes, sleep quality, and personal/work-related factors. Understanding the work environment's role and assessing interventions targeting working conditions could mitigate dissociation and promote a trauma-informed workplace. Rigorous study designs are needed to generate stronger evidence for nonpharmacological interventions like coherent breathing.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".