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Record W2950231469 · doi:10.1097/gme.0000000000001352

Public survey reactions to an arts-based educational menopausal hot flash exhibit

2019· article· en· W2950231469 on OpenAlexaff
Janet S. Carpenter, Kevin L. Rand, Karen Schmidt, Jennifer Lapum, Mark D. Kesling

Bibliographic record

VenueMenopause The Journal of The North American Menopause Society · 2019
Typearticle
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMedicineHot flashFlash (photography)Visual artsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to obtain public survey reactions to concept art for an exhibit about menopausal hot flashes designed to stimulate learning, dispel myths, spur dialogue, and increase empathy. METHODS: Immediately before viewing the art, participants provided demographic information and answered one open-ended question. Immediately after viewing the art, participants answered the same open-ended question, one additional open-ended question, and completed quantitative survey questions. RESULTS: Overall, public reactions to the concept art were positive. Qualitative and quantitative data indicated that the public thought the exhibit was appealing, stimulated learning, dispelled myths, spurred desire to have conversations about hot flashes, and increased empathy for women with menopausal hot flashes. CONCLUSIONS: The exhibit concept art was appealing and was reported to have a positive impact on the public. Study findings provide support for building the exhibit full-scale as a traveling educational resource that might change public discourse around menopausal hot flashes.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.062
GPT teacher head0.346
Teacher spread0.283 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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