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Record W4282945584 · doi:10.1080/17482631.2022.2075532

“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

2022· article· en· W4282945584 on OpenAlexafffundabout
Charlene H. Chu, Amanda Yee, Vivian Stamatopoulos

Bibliographic record

VenueInternational Journal of Qualitative Studies on Health and Well-Being · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsOntario Tech UniversityToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCentre for Aging + Brain Health Innovation
KeywordsThematic analysisEmotional traumaPsychologyNursingMedicineSociologyPsychiatryQualitative researchSocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.472
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2022
Admission routes3
Has abstractyes

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Same venueInternational Journal of Qualitative Studies on Health and Well-BeingSame topicGeriatric Care and Nursing HomesFrench-language works237,207