“It’s the worst thing I’ve ever been put through in my life”: the trauma experienced by essential family caregivers of loved ones in long-term care during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
BACKGROUND: Essential family caregivers (EFCs) of relatives living in long-term care homes (LTCHs) experienced restricted access to their relatives due to COVID-19 visitation policies. Residents' experiences of separation have been widely documented; yet, few have focused on EFCs' traumatic experiences during the pandemic. Objective: This study aims to explore the EFCs' trauma of being locked out of LTCHs and unable to visit their loved ones in-person during COVID-19. METHODS: Seven online focus groups with a total of 30 EFCs from Ontario and British Columbia, Canada were conducted as part of a larger mixed-method study. We used an inductive approach to thematic analysis to understand the lived experiences of trauma. RESULTS: Four trauma-related themes emerged: 1) trauma from prolonged separation from loved ones; 2) trauma from uncompassionate interactions with the LTCH's staff and administrators; 3) trauma from the inability to provide care to loved ones, and 4) trauma from experiencing prolonged powerlessness and helplessness. DISCUSSION: The EFCs experienced a collective trauma that deeply impacted their relationships with their relatives as well as their perception of the LTC system. Experiences endured by EFCs highlighted policy and practice changes, including the need for trauma-centred approaches to repair relational damage and post-pandemic decision-making that collaborates with EFCs.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".