Care Aides’ Perceptions of Caring for Nursing Home Residents With Past Psychological Trauma
Bibliographic record
Abstract
Abstract The dynamic interplay between dementia and psychological trauma can exert powerful effects on nursing home residents’ behavioral symptoms and quality of life. Our objectives in this exploratory study were to assess care aides’ perceptions of how often they worked with residents with past psychological trauma, the types of trauma encountered, and reasons for these beliefs. We conducted semi-structured cognitive interviews (n = 10) with care aides in June 2019 to inform the development of a trauma needs assessment (4 questions) that we included in a large survey of nursing staff (2019 - 2020). Care aides (n = 3761) were sampled from 91 randomly selected urban nursing homes stratified by health region, owner operator model, and size. We completed basic statistics and content analyses. Care aides identified residents they believed to have psychological trauma histories and provided reasons for their beliefs. Approximately 12% of the reported traumatic events were disclosed to staff. The most common, broad categories of trauma to emerge during analysis were abuse (40%) and war exposure (30%). Each had sub-categories. The most common categories of signs of trauma were re-experiencing symptoms such as flashbacks and nightmares (28%), and avoidance of specific triggers, such as water or intimate care (24%). The majority of the reported signs of trauma were persistent and distressing for staff and residents. Some behaviours assumed to be responsive behaviours of dementia may relate to traumatic stress symptomatology. Implementing trauma-informed supports for residents and care aides is essential to person-centred care and optimal quality of life.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".