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Record W4400823401 · doi:10.32920/jcd.v7i2.1983

Artful Compassion

2024· article· en· W4400823401 on OpenAlexaffvenue
Phillip Joy, Megan White, Megan Aston

Bibliographic record

VenueJournal of Critical Dietetics · 2024
Typearticle
Languageen
FieldPsychology
TopicLeadership, Courage, and Heroism Studies
Canadian institutionsDalhousie UniversityMount Saint Vincent University
Fundersnot available
KeywordsCompassionPsychologyPsychoanalysisArtPhilosophyTheology

Abstract

fetched live from OpenAlex

This research explores experiences of compassion in the eating disorder recovery processes of 2S/LGBTQ+ people. There exists a growing body of evidence suggesting disparities in the assessment, treatment, and overall care of eating disorders in 2S/LGBTQ+ communities. One concern is a potential lack of compassion, which can exacerbate feelings of isolation for 2S/LGBTQ+ individuals. To gain a deeper understanding of this issue, we embrace a queer poststructuralist approach to our research that disrupts traditional knowledge and acknowledges experiences as socially constructed. Semi-structured interviews with 14 self-identifying 2S/LGBTQ+ people who have experienced eating disorder care were conducted. Analysis of the data revealed three main types of experiences: 1) Experiences of otherness, 2) Experiences of compassion in eating disorder care, and 3) Experiences of compassion in queer communities. To “queer” our findings, we present them through graphic art and non-traditional scientific writing. The graphic art represents participants’ experiences and is followed by critical discussions that further explore the socially constructed nature of their experiences. Our findings underscore the critical need for enhanced compassion for 2S/LGBTQ+ people during treatment with eating disorders.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.013
Scholarly communication0.0060.004
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.002

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 head0.068
GPT teacher head0.429
Teacher spread0.361 · 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 designNot applicable
Domainnot available
GenreOther

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 routes2
Has abstractyes

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