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Record W4309665855 · doi:10.1177/15248399221133734

Wicked Bodies: A Health and Well-Being Toolkit Addressing Eating Disorders Within LGBTQIA2S+ Communities

2022· article· en· W4309665855 on OpenAlexaff
Phillip Joy, Olivier Ferlatte, Megan Aston, Hannah Minzloff, John Hillis

Bibliographic record

VenueHealth Promotion Practice · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsDalhousie UniversityUniversité de MontréalMount Saint Vincent University
Fundersnot available
KeywordsTransgenderQueerLesbianTransformative learningCompassionSociocultural evolutionLiminalityPsychologyGender studiesSociologyDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

Wicked Bodies is a toolkit for addressing eating disorder in LGBTQIA2S+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two-Spirit, and other sexual and gender minority) communities, an increasing prevalent issue that can have serious consequences on the health and well-being for LGBTQIA2S+ people. The toolkit consists of a series of short films and a discussion guidebook that provide a template that can be used for engaging with LGBTQIA2S+ youth through a lens of compassion and cultural humility. Wicked Bodies does this by presenting the lived experiences of a diverse range of LGBTQIA2S+ individuals navigating sociocultural pressures, gender expectations, and peer-based ideals around body weight and shape. Feedback from three screening events revealed that Wicked Bodies has the potential to be transformative as a health promotion initiative.

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)0.005
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.003

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 head0.080
GPT teacher head0.414
Teacher spread0.333 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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