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Record W4399793195 · doi:10.1093/jsxmed/qdae054.081

(086) MEDICAL GASLIGHTING AND VULVOVAGINAL PAIN DISORDERS

2024· article· en· W4399793195 on OpenAlexaff
Chailee Moss, G Skovronsky, J Holloway, S Lorenzini, I Kopits, Mollie Rieff, Lloyd G. Mitchell, J Krapf, A Goldstein

Bibliographic record

VenueThe Journal of Sexual Medicine · 2024
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsQueen's University
Fundersnot available
KeywordsVulvodyniaInjusticeFibromyalgiaReferralContext (archaeology)MedicineFamily medicinePelvic painPsychologyPsychiatrySocial psychologySurgery

Abstract

fetched live from OpenAlex

Abstract Introduction Gaslighting is a form of epistemic injustice that involves testimonial injustice, in which patient’s experiences are discounted by others. The incidence of epistemic injustice has been described in a number of pain conditions including chronic fatigue syndrome and fibromyalgia, as well as in marginalized obstetric populations. Little research has been directed towards epistemic injustice in populations seeking treatment for benign gynecologic conditions or the unique context of vulvovaginal pain conditions. Additionally, little is known about the consequence of this type of injustice on patient’s perception of their care or willingness to seek care. Objective The purpose of this study was to develop a patient-validated instrument to measure patient experience of epistemic injustice in their past gynecologic care and to use the instrument to explore patient experiences in a vulvovaginal pain referral center. Methods An instrument was designed by the study team to quantify the incidence and distressed caused by gaslighting in patients seek care for vulvovaginal pain conditions. This was created based on common themes from written testimonials of patients available from National Vulvodynia Association. After construction, the instrument was submitted for feedback to officers from two vulvovaginal pain disorder patient societies. The survey was modified to include the feedback from these sources. The survey was then completed by patients establishing care with the clinic prior to their first appointment. Results 187 patients completed the survey in part or in full. Patients reported having seen an average of 5.7 providers prior to presentation. Patients felt supported by only 42% of past providers. Patients felt belittled by 27% of past providers. Patients felt 21% of past providers did not believe their symptoms. 45% of patients were told they “just needed to relax more”; 18% percent were recommended to drink alcohol to improve their condition; 55% considered giving up seeking care because they felt their concerns were not being addressed; 31% were told their symptoms were caused by high levels of anxiety; 22% were referred to psychiatry without other medical treatment for their symptoms; 19% felt unsafe during a medical encounter. 39% percent of patients were made to feel they were “crazy” and this was the most distressing of the questions asked (average 7.3/10 on a likert scale for distress for patients who had experienced this). Conclusions A survey based on patient concerns and validated by patients affected by vulvovaginal pain conditions has not been previously reported. This instrument was applied to a patient population at a large referral center for vulvovaginal pain conditions and the results showed that patients commonly experienced many forms of epistemic injustice, and greater than half of patients consider cessation of care for their condition because of these experiences. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Dr. Jill Krapf is a medical advisor for Evvy. Dr. Andrew Goldstein is a part time Employee of Daré Bioscience. He is a member of the board of directors of The Gynecologic Cancers Research Foundation. He is an advisor for: AbbVie, Incyte, and the National Vulvodynia Association. He has received research funding from Incyte.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.325
Teacher spread0.312 · 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 teacher head, not a consensus.

Study designOther design
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
Published2024
Admission routes1
Has abstractyes

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