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Record W2575451560 · doi:10.1177/084456211404600302

Shadows and Sunshine: What Metaphors Reveal about Aging with HIV

2014· article· en· W2575451560 on OpenAlexaffvenue
Rosanne Beuthin, Laurene Sheilds, Anne Bruce

Bibliographic record

VenueCanadian Journal of Nursing Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of VictoriaIsland Health
Fundersnot available
KeywordsHonourNarrativeHuman immunodeficiency virus (HIV)HumanitiesPersonhoodIdentity (music)PsychologyPsychoanalysisArtAestheticsMedicinePhilosophyHistoryLiteratureEpistemology

Abstract

fetched live from OpenAlex

Using narrative inquiry, the researchers interviewed 5 older adults on 5 occasions over a period of 3.5 years about their experiences of aging with HIV. The participants' stories were analyzed for metaphors. Individual metaphors reveal a complex, unique struggle: living between tensions of uncertainty and hope, facing death and living in the moment, and suffering hurt amidst the joys of evolving identity. The tensions are fluid, although time and life experience facilitate a shift towards reconciliation. An overarching metaphor across this group of survivors is shadows and sunshine: to survive and live in a fragile state, balancing multiple shadows such as stigma and side effects with joyful experiences of support and belonging. The findings suggest that when nurses invite stories of life experience and listen for language used, they build compassion and gain understanding of what support is most needed to honour the personhood of older adults who are HIV-positive.

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)0.007
metaresearch head score (Gemma)0.016
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 score0.009
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.012
Scholarly communication0.0060.009
Open science0.0010.007
Research integrity0.0020.003
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 head0.120
GPT teacher head0.476
Teacher spread0.357 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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