Presence and Role of Associations of Cancer Patients and Volunteers in Specialist Breast Centres: An Italian National Survey of Breast Centres Associated with Senonetwork
Bibliographic record
Abstract
This article aims to present the results of a national, cross-sectional, voluntary, online survey on the presence and roles of associations of breast cancer patients and volunteers in Italian specialist breast centres. The survey was developed according to standard methods. The questionnaire was pre-tested by a random sample of three breast centres, loaded onto the SurveyMonkey platform, and piloted by one volunteer breast centre. The breast centre clinical leads were invited to participate via email. A link to the online instrument was provided. No financial incentives were offered. The results were reported using standard descriptive statistics. The response rate was 82/128 (65%). Members of associations were routinely present in 70% Italian breast centres. Breast centres most often reporting their presence were those certified by the European Society of Breast Cancer Specialists. Patient support (reception and information, listening, identification of needs, and psychological support) was the primary area where associations were reported to offer services. The magnitude of this phenomenon warrants a study to investigate the impact of the activities of associations on the quality of life of patients and on the cost-benefit ratio of the service, and the modes of their interactions with the nursing staff and the medical staff.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".