The HOPE study: evaluating the impact of an online educational resource for heavy menstrual bleeding on the patient–physician dynamic
Bibliographic record
Abstract
The Heavy menstrual bleeding: Evidence-based Learning for best Practice (HELP) Group developed an educational website about heavy menstrual bleeding (HMB). The "HMB improving Outcomes with Patient counseling and Education" (HOPE) project examined the website's impact on women's knowledge, confidence, and consultations with healthcare providers (HCPs). HOPE was a quantitative online survey of gynecologists and women with HMB in Brazil. After an initial consultation, patients had unlimited access to the website and completed a survey. HCPs also completed a survey about the sconsultation. After a second consultation, HCPs and patients completed another survey. HCP surveys assessed their perception of patients' awareness, understanding, and willingness to discuss HMB. Patient surveys assessed their knowledge, experience, and confidence in discussing HMB. Forty HCPs recruited 400 women with HMB. Based on HCP perceptions at the first consultation, 18 percent of patients had "good knowledge" or "very good knowledge" of HMB, increasing to 69 percent after patients had visited the website. Before and after visiting the website, 34 percent and 69 percent of patients, respectively, regarded their HMB knowledge as "goo.d" Additionally, 17 percent of women reported their anxiety as "highest" during the first consultation; this decreased to 7 percent during the second consultation. After visiting the HELP website, patients' knowledge of HMB improved and they were less anxious.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".