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Record W4387159002 · doi:10.1111/jan.15888

Insight into the experiences of caregivers of older adults in long‐term care homes: A photovoice study

2023· article· en· W4387159002 on OpenAlexaffabout
Sheila A. Boamah, Marie‐Lee Yous, Harrison Gao, Rachel Weldrick, Vanina Dal Bello‐Haas, Pamela Durepos

Bibliographic record

VenueJournal of Advanced Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New BrunswickSimon Fraser UniversityWestern UniversityMcMaster University
Fundersnot available
KeywordsPhotovoiceFeelingThematic analysisMental healthDisappointmentPsychologyFocus groupNursingDistressLong-term careMedicineQualitative researchPsychiatryClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

AIMS: To explore the lived experiences of caregivers of people living in long-term care (LTC) homes during the initial phases of the COVID-19 pandemic and potential supports and resources needed to improve caregivers' quality of life. BACKGROUND: Carers (or care partners) of adults in LTC contribute substantially to the health and well-being of their loved ones by providing physical care, emotional support and companionship. Despite their critical role, little is known about how caregivers have been impacted by the pandemic. DESIGN: An interpretive descriptive approach that incorporated the photovoice method was used. METHODS: Using a purposive sampling strategy, six family carers in Ontario, Canada were recruited between September and December 2021. Over a 4-week period, caregivers took pictures depicting their experience of the pandemic that were shared in a virtual focus group. Visual and text data were analysed using thematic analysis with an inductive approach. FINDINGS: Caregivers expressed feelings of frustration, confusion and joy. Emerging themes included: (i) feeling like a 'criminal' amidst visitor restrictions and rules; (ii) experiencing uncertainty and disappointment in the quality of care of long-term care homes; (iii) going through burnout; and (iv) focusing on small joys and cherished memories. CONCLUSIONS: The combination of visual and textual methods provided unique insight into the mental distress, isolation and intense emotional burdens experienced by caregivers during the pandemic. IMPACT: Our findings underscore the need for LTC organizations to work in unison with caregivers to optimize the care of residents and support the mental health of caregivers. REPORTING METHOD: This work adhered to the consolidated criteria for reporting qualitative research (COREQ) checklist. PUBLIC CONTRIBUTIONS: The caregivers included in the study were involved in the co-creative process as active contributors informing the design and validation of the codes and themes.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.389
Teacher spread0.370 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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