A Preliminary Qualitative Study on Women's Experiences of Bleeding Disorders in Italy
Bibliographic record
Abstract
Introduction: Haemophilia continues to hold the image of a male disease, yet Bleeding Disorders affect women on multiple levels, as carers, genetic-carriers, and patients. In Italy there are no specific patient groups to address women’s issues and perspectives of the disease. There is a great need for qualitative research on the key issues, and potential resources, for women with Bleeding Disorders. \nMethods: A review of international patient groups that address women and Bleeding Disorders was conducted. This established the key themes for these groups, primarily in Canada and the USA. Based on this framework a 20 question semi-structured interview was developed that addressed general experience and reproductive issues. Up to now 22 women were asked in person to participate in the project and received letter explaining the research aims in detail. These women were interviewed over a 5 month period: 12 were healthy genetic carriers and parents of Bleeding Disorder patients; 2 were a symptomatic carriers of Haemophilia (1 severe); 2 had deficit of factor XI; 5 had Von Willebrand disease (1 plus mild Factor XI deficiency); and 1 had Glazmann disease. The interviews lasted around 75 min. The results of the interviews were then analysed in correspondence with issue presented by the Anglophone patient groups. \nResults: The majority of the key issues raised by the Anglophone patient groups were also present in the Italian interview subjects. The patient group indicated that there is still a several year lag between first symptoms and diagnosis, directly related to a continued belief in the general medical population that Bleeding Disorders are male diseases. Other areas that need to be addressed further are: awareness of “normal” menstruation; genetic transmission; reproductive resources and non-specialist care. All participants indicated the fundamental role of the specialized Bleeding Disorder centre in their overall physical and emotional well-being. \nDiscussion/Conclusion: The specific areas of care that need to be further addressed for Italian women Bleeding Disorder patients correspond for the most part with their Anglophone counter-parts, with special nuances regarding reproductive issues. Yet these have few Italian language resources and therefore are greatly reliant on specialized centres for information. Multi-language informational resources for women are needed.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".