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Record W4403541206 · doi:10.1080/15381501.2024.2413497

“Tears turning to diamonds:” creating empathic understanding toward women living with HIV through contemplative viewing of a virtual art exhibition

2024· article· en· W4403541206 on OpenAlexaff
Erin Mowbray, Jane Costello, Patricia Morgan, Asha Persson, Katherine Boydell, Deborah Bateson, Kath Leane, Agatha An, Sally Nathan, Christy E. Newman, Allison Carter

Bibliographic record

VenueJournal of HIV/AIDS & Social Services · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsContemplationExhibitionTearsHuman immunodeficiency virus (HIV)ArtAestheticsPsychologyVisual artsMedicinePhilosophyVirologyTheology

Abstract

fetched live from OpenAlex

We explored changes in HIV knowledge and attitudes among audiences of an exhibition about women living with HIV (Information removed to maintain peer-review integrity). Six tours were facilitated with three groups: women living with HIV, health and social care providers, and the general public. Thirty participants were guided through a meditative breathing exercise prior to viewing five artworks from the exhibition through the contemplative process of “deep looking.” Participants were prompted to write and draw in response to their feelings and thoughts about each artwork. Four themes were identified: the first described how tours worked to elicit emotional responses; the second described the power of art to convey lived experiences and provoke new understandings; the third indicated a transformation in knowledge and attitudes; and the fourth highlighted many participants’ desires to effect change and reduce stigma. Creative and contemplative practices can make valuable contributions to public health by fostering empathic understanding toward marginalized groups.

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)0.004
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.294
GPT teacher head0.502
Teacher spread0.208 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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