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Record W4282918008 · doi:10.1177/10748407221100284

Exploring the Impacts of COVID-19 Public Health Measures on Community-Dwelling People Living With Dementia and Their Family Caregivers: A Longitudinal, Qualitative Study

2022· article· en· W4282918008 on OpenAlexafffundabout
Jennifer Baumbusch, Heather A. Cooke, Kishore Seetharaman, Aneesa Khan, Koushambhi Basu Khan

Bibliographic record

VenueJournal of Family Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersInstitute of Gender and HealthCanadian Institutes of Health Research
KeywordsPandemicDementiaPublic healthContext (archaeology)Qualitative researchMental healthGerontologyHarmLongitudinal studyAging in placePsychologyCoronavirus disease 2019 (COVID-19)MedicineNursingSociologyPsychiatrySocial psychologyGeography

Abstract

fetched live from OpenAlex

Since the onset of the COVID-19 pandemic, community-dwelling people living with dementia and their family caregivers have experienced many challenges. The unanticipated consequences of public health measures have impacted these families in a myriad of ways. In this interpretive policy analysis, which used a longitudinal, qualitative methodology, we purposively recruited 12 families in British Columbia, Canada, to explore the impacts of pandemic public health measures over time. Semi-structured interviews were conducted every 3 months and participants completed diary entries. Twenty-eight interviews and 34 diary entries were thematically analyzed. The findings explore ways that families adopted and adapted to public health measures, loss of supports, both formal and informal, and the subsequent consequences for their mental and physical well-being. Within the ongoing context of the pandemic, as well as potential future wide-spread emergencies, it is imperative that programs and supports are restarted and maintained to avoid further harm to these families.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.367
GPT teacher head0.453
Teacher spread0.086 · 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.

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

Citations15
Published2022
Admission routes3
Has abstractyes

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