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Record W2944288973 · doi:10.1177/0733464819848634

The Role of Information and Communication Technology in End-of-Life Planning Among a Sample of Canadian LGBT Older Adults

2019· article· en· W2944288973 on OpenAlexafffundabout
Steven E. Mock, Earl Walker, Áine M. Humble, Brian de Vries, Gloria Gutman, Jacqueline Gahagan, Line Chamberland, Patrick Aubert, Janet Fast

Bibliographic record

VenueJournal of Applied Gerontology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of AlbertaDalhousie UniversitySimon Fraser UniversityUniversité du Québec à MontréalMount Saint Vincent UniversityUniversity of Waterloo
FundersEconomic and Social Research CouncilCanadian Frailty Network
KeywordsPsychologySituational ethicsLesbianUsabilityFocus groupSample (material)Social psychologyApplied psychologyGerontologySociologyMedicineComputer science

Abstract

fetched live from OpenAlex

To better understand the role of technology in later-life planning among older lesbian, gay, bisexual, and trans (LGBT) adults, we conducted focus groups to explore factors linked to diverse sexual orientations and gender identities. Twenty focus groups were facilitated across Canada with 93 participants aged 55 to 89. Constant comparative analysis yielded four categories: (a) fear, (b) individual benefits, (d) social elements, and (d) contextual elements. Fear related to technology and fear of end-of-life planning. Individual benefits referred to technology as a platform for developing LGBT identities and as a source of information for later-life planning. Social elements were establishment and maintenance of personal relationships and social support networks. Contextual elements referred to physical and situational barriers to technology use that limited access and usability. These findings can inform technological practice and services to enhance later-life planning.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2019
Admission routes3
Has abstractyes

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