MétaCan
Menu
Back to cohort
Record W4292361964 · doi:10.1001/amajethics.2022.740

Underrecognition of Dysmenorrhea Is an Iatrogenic Harm

2022· article· en· W4292361964 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe AMA Journal of Ethic · 2022
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsHarmInjusticeMenstruationStigma (botany)Health careEconomic JusticeMedicineSocial stigmaPsychologyPsychiatryHealth professionalsFace (sociological concept)NursingFamily medicineSocial psychologyPolitical scienceSociologyPathology

Abstract

fetched live from OpenAlex

Many patients face years of recurrent and debilitating menstrual pain that affects their ability to work and study. Patients often normalize their severe pain as an expected part of menses. Both underrecognition and lack of awareness of available therapies for this remediable condition serve as a quintessential example of hermeneutic injustice. Hermeneutic injustice describes a structural lack of access to epistemic resources, such as shared concepts and knowledge. Pervasive menstrual stigma further discourages people with dysmenorrhea from discussing their symptoms and seeking health care. A lack of respect for women's experiences of pain in clinical encounters acts to worsen these issues and should be considered a source of iatrogenic harm. Health care workers can promote hermeneutic justice by preemptively destigmatizing discussions about menstruation and validating patients' concerns. On a systemic level, there should be greater awareness of dysmenorrhea and the various treatments availabe for it.

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.

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)0.001
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.102
GPT teacher head0.379
Teacher spread0.276 · 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