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Record W6888769248 · doi:10.21427/e2qd-kj23

“I know a lot of people just gave up and died, out of loneliness”: family members experiences of COVID-19 visitation restrictions in Irish nursing homes

2025· article· en· W6888769248 on OpenAlexaboutno aff

Bibliographic record

VenueArrow - TU Dublin (Technological University Dublin) · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisIrishPandemicReflexivityPower (physics)Government (linguistics)Nursing homesQuality (philosophy)

Abstract

fetched live from OpenAlex

COVID-19 has had a disproportionate impact on older adults, particularly those living in nursing homes (NH) globally and in Ireland, the site of our study. Whilst NH residents were at increased risk of serious illness from the virus, visitation bans and restrictions also impacted residents' quality of life. Family members' (FMs) essential caring role was recognised in contexts like the Netherlands, parts of the United States and Ontario, Canada, but were not in Ireland. The current study explored the experiences of FMs of residents who experienced visitation bans during the first four waves of the COVID-19 pandemic in Ireland. Eight female FMs of a resident in a NH were interviewed using semi-structured interviews. The transcripts were analysed using reflexive thematic analysis and two core themes were generated of 1. Navigating the New Normal and 2. Impacts of being locked-in and locked-out. The analysis demonstrates that “guidelines” on visitation to NHs in Ireland were not always adhered to, leading to prolonged family separation. FMs describe how being locked-out, and residents being locked-in, impacted residents physically and psychologically, and FMs wellbeing. The analysis also illustrates the importance of family connection, the power imbalance between residents and NHs who enforced prolonged bans, and of FMs as advocates and carers for residents. It also indicates the importance of policy and practice responses that protect the essential caregiving role of FMs, and the rights of NH residents, in future waves of the pandemic and future crises. Policy and practice implications are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.379
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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