DonnaRosa Project: Exploring Informal Communication Practices Among Breast Cancer Specialists
Bibliographic record
Abstract
Background: Healthcare communication often relies on complex digital infrastructures, yet clinicians increasingly adopt general-purpose Instant Messaging Apps (IMAs) such as WhatsApp® to meet unmet needs. DonnaRosa, an Italian community of breast cancer specialists founded in 2017, is a Community of Practice (CoP), where experts exchange second opinions, guidelines, and trial opportunities. This paper examines its origins, practices, and implications. Methods: A mixed-methods design was applied: (1) qualitative analysis of chat logs to identify interaction patterns and rules; (2) a 2024 online survey of 54 members (92.5% response rate) exploring demographics, usage, and perceived value; (3) ongoing semi-structured interviews with founders and participants to reconstruct history, recruitment, and professional impact. Results: The group has grown through personal invitations, creating a friendly network of oncologists. Communication is concise, colloquial, and collegial. Activities focus on case discussions, reassurance, interpretation of guidelines, and exchange of research opportunities. This article presents data from an online survey conducted in 2024, showing that the group is widely used for second opinions, often consulted even on weekends and holidays, and perceived as a source of professional support and learning. Members report that participation frequently changes or refines their clinical judgement, especially when guidelines are incomplete or ambiguous. The community also promotes resilience, reduces professional isolation, supports informal collaboration in research projects, and encourages interaction on organisational and healthcare management issues. Conclusions:DonnaRosa illustrates how informal IMAs can evolve into robust infrastructures of care and professional solidarity, complementing formal systems. In the era of artificial intelligence, CoPs like DonnaRosa may become even more relevant: AI tools, especially large language models, can accelerate literature retrieval and data synthesis, while the CoP provides the critical, experience-based interpretation needed for safe and meaningful application. Such a dual infrastructure—technological and human—offers a promising path for oncology, where complexity requires both computational breadth and the depth of expert clinical judgement. Taken together, these findings and the evolving role of AI in clinical communication underscore the need for oncology societies to develop governance frameworks that ensure the safe, accountable, and clinically appropriate use of instant-messaging tools in professional practice.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".