Is the patients’ fear of cancer the main barrier to prescribing menopausal hormone therapy?
Bibliographic record
Abstract
OBJECTIVE: Menopausal hormone therapy (MHT) is the most effective treatment for relieving menopausal symptoms. However, many women avoid this therapy due to fear, and in Brazil numerous cities lack access to this treatment in the public health system. This study aimed to investigate prescribing habits regarding MHT among gynecologist-obstetricians in the Brazilian public versus private health systems, and to identify the main barriers to its use. METHOD: This descriptive cross-sectional study utilized a quantitative approach. Gynecologist-obstetricians from across Brazil were invited to complete a structured electronic questionnaire assessing their prescribing practices in both the public and private health sectors. RESULT: A total of 433 valid responses were analyzed. Among them, 51.5% of participants reported providing care to climacteric patients in the public health system, with 46.2% working in both sectors. Among physicians practicing in both settings, 76.5% reported prescribing MHT more frequently in the private sector. The main barriers to MHT prescription in the public system were treatment cost (68.2%) and lack of availability of free medication (61.4%), while in the private system the predominant barriers were fear of therapy-related risks (93.6%), especially cancer. Only 27.8% reported free access to MHT in their cities. CONCLUSION: The findings indicate that MHT prescribing practices in Brazil are still significantly influenced by structural barriers in the public sector and by negative perceptions in the private sector. Interventions aimed at expanding access and educating both physicians and patients are essential to ensure safe and equitable use of MHT.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".