“There Is so Much Power in Just Believing Someone”: Understanding the Lived Experiences of Women with Heavy Vaginal Blood Loss
Bibliographic record
Abstract
Background: More than half of reproductive-age women experience heavy vaginal blood loss (VBL), whether menstrual, lochial, or otherwise, yet less than 10% seek medical attention for evaluation. In literature, those who experience heavy VBL often live with stigma, multiple uncertainties around their diagnosis, interference with day-to-day activities such as school and work, and a lack of clarity of what constitutes ‘normal’ bleeding. Over the past 100 years, at a population level, we are noticing earlier menarche, fewer pregnancies, later menopause which leads to longer periods of bleeding. This makes heavy vaginal blood loss under-recognized, under-diagnosed, serially misrecognized, and often dismissed by healthcare providers. It is well documented that there are widespread knowledge and care gaps surrounding VBL due to structural multidimensional stigma. There is an urgent need to better evaluate heavy vaginal blood loss that is informed by patient voices. Objective: Documenting the experiences of patients with vaginal bleeding using qualitative methods and subsequently implement the findings using targeted multimodal knowledge-translation-exchange interventions including incorporation into medical education curricula. Methods: Recruitment of self-identified women from St. Michael's Hospital Hematology Clinic in Toronto, Ontario, Canada to elucidate narratives on the dimensions of vaginal bleeding-related experiences and healthcare interactions. Ethics was obtained through Unity Health Toronto Research Ethics Board (REB 21-126). Eligibility was determined by both clinical and self-reported heavy vaginal bleeding and complications. We used semi-structured interview guide informed by literature to conduct the interviews lasting 35 - 50 minutes using telecommunication to ensure we achieve thematic saturation. Interviews were audio-recorded, transcribed verbatim, with coding and thematic analysis completed. Results: Analysis of n = 10 self-identified women with heavy vaginal bleeding with demographics yielding equitable distribution across race and ethnicity with 50% White women, 40% Black women, and 10% Asian women. At least 50% had a Bachelor's degree and 20% had a Graduate degree. Duration of excessive bleeding was over 10 years for 90% of women with prolonged time to referral to specialist whether Hematology or Obstetrics/Gynecology for further evaluation. All participants experienced pain with heavy vaginal bleeding and were iron deficient as evidenced by iron supplementation whether oral or intravenous. Major themes include: shame around bleeding symptom, self-doubt, concerns of over-disclosing, normalization of heavy vaginal bleeding pathology which often led to delayed diagnoses and treatment, dismissal of symptoms disclosed leading to mistrust of healthcare system, and paradoxically the fear of “feeling better”. Discussion: Heavy VBL is exceedingly common, has important clinical and psychosocial ramifications, yet it continues to be stigmatized, underdiagnosed, and thus poorly treated. Findings of our study indicate themes of shame, normalization of pathology, mistrust of healthcare systems, fear of feeling better. Structural sexism and discrimination lead to normalization, desensitization, dismissal and blame placement. Targeted knowledge translation rooted in theory- and evidence-based implementation science is urgently required in this space to break cycles of harm and injustice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".