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Record W4387866364 · doi:10.1017/s0714980823000636

Older Adults’ Experiences with Remote Care for Specialized Health Service During the COVID-19 Pandemic: A Descriptive Qualitative Study

2023· article· en· W4387866364 on OpenAlexaffabout
Alice Gaudine, Karen Parsons, Joanne Smith-Young

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPandemicFeelingQualitative researchDescriptive researchHealth careTelehealthMedicineCoronavirus disease 2019 (COVID-19)Descriptive statisticsTelemedicineNursingGerontologyPsychologyDiseaseSociologySocial psychology

Abstract

fetched live from OpenAlex

The coronavirus disease (COVID-19) pandemic necessitated a rapid uptake of remote health care services. This qualitative descriptive study was designed to gain an understanding of older adults' experiences of remote care (telephone or online video conference appointments) for specialized health services during the COVID-19 pandemic. Twenty-one older adults (ages 65 years and older; 8 men and 13 women) living in eastern Canada participated in a semi-structured telephone interview. Data were analysed using qualitative content analysis. The vast majority of older adults were overall satisfied with their remote experiences of specialist care. Advantages to remote care for specialized services included convenience, safety during the pandemic, comfort, efficiency, and ease of visit. Disadvantages included communication not as effective, feeling depersonalized or disembodied, missing the human relationship, and wanting reassurance of physical assessment. It is important that health professionals understand the disadvantages for older adults of remote care visits in order to mitigate them.

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)0.008
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 score0.963
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0030.003
Open science0.0010.004
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 head0.059
GPT teacher head0.359
Teacher spread0.301 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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