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Record W4398147765 · doi:10.1016/j.diggeo.2024.100091

Digital and analogue spaces of care: How older adults are redefining care practices in the COVID-19 pandemic

2024· article· en· W4398147765 on OpenAlexaffabout
Christine Gibb, Gabriella Meltzer, Nnenia Campbell, Alice Fothergill

Bibliographic record

VenueDigital Geography and Society · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Environmental Health Sciences
KeywordsPandemicSocial distanceVulnerability (computing)NegotiationHealth careDigital healthIntersectionalityCoronavirus disease 2019 (COVID-19)Public relationsInternet privacyPolitical scienceSociologyPsychologyMedicineGender studiesComputer securityComputer scienceSocial science

Abstract

fetched live from OpenAlex

COVID-19 changed the way we care. Scholars have long argued that care often requires proximity, especially when it comes to care for, with, and by older adults. With lockdowns and the imposition of widespread public health guidelines aimed at curbing the spread of COVID-19, such as physically distancing and sheltering-in-place, in-person care practices became increasingly difficult. Yet, unlike disasters catalyzed by hurricanes or other natural hazards, physical and communications infrastructures remained largely intact during the pandemic. This situation opened the possibility for shifting care into digital spaces. In this paper, we study how older adults (ages 65 and up) in Canada and the USA navigated this abrupt turn towards digital spaces for care. Our findings are drawn from our larger mixed methods study investigating the everyday COVID-19 pandemic experiences of older adults, children, and teens, examining vulnerability, mobilities, and capacities. Not only are older adults frequently characterized as the recipients of care, but they are also typically (and erroneously) homogenized and stereotyped as vulnerable and tech-unsavvy. Exploring the ways in which older adults have provided, sought, received, avoided, and been denied care during the pandemic thus reveals the complex negotiations, contestations, and emancipatory possibilities of digital spaces of care. Our attention to the accessibility needs of diverse older adults serves as a vehicle for exploring issues of intersectionality in shaping digital care. We describe a range of digital care practices, ranging from telemedicine appointments and app-based communication to web-based volunteering and online social gatherings. We explore digital communication and connection between generations; the potential for such communication during the COVID-19 pandemic is unprecedented, in part due to the massive uptake of digital communication options such as online video conferencing programs. We discuss the mismatch between the possibilities made available through digital architectures and care practices, relations, needs, and desires of older adults. Drawing on feminist theorizations of care, we situate older adults as both givers and receivers of digital care and unpack the intertwining of their agency and vulnerability. Their innovations, spurred in part by diverse experiences with the aging process, the pandemic, loneliness, joy, and frustrations with care in the digital sphere, suggest radical practices and spaces for inclusive care during and after the pandemic. What is radical about such care is that it is based on everyday, even mundane, elements that often go unremarked, rather than any flashy (monetized) innovations developed by technology companies.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.014
Scholarly communication0.0090.008
Open science0.0010.012
Research integrity0.0020.003
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 head0.033
GPT teacher head0.336
Teacher spread0.303 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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