‘Nothing fit me’: nationwide consultations with young women with breast cancer
Bibliographic record
Abstract
OBJECTIVE: There exists little research about the experience of breast cancer for young women in Canada. To address this gap, the Canadian Breast Cancer Network (CBCN) and the Ontario Breast Cancer Community Research Initiative undertook a research project to explore the information and support experiences, needs and recommendations of geographically diverse Canadian young women with breast cancer. SETTING AND PARTICIPANTS: We consulted with 65 young women in 10 focus groups held across Canada. All women had been diagnosed with breast cancer at, or before, 45 years of age. During the consultations the women were asked to discuss their information and support experiences and needs, as well as resource recommendations related to their diagnosis, treatment and survivorship. MAIN RESULTS: The overarching theme, 'Nothing Fit Me', revealed that accessed information, support and programmes/services did not 'fit' or match the women's age or life stage. When we asked for their recommendations the young women suggested that information and support match their age and life stage and that health-care providers create and implement several topical workshops concerning, for example, sexuality, lymphedema and reconstruction. CONCLUSION: The findings will be used by the CBCN as a general platform from which to conduct further research and/or action strategies. The CBCN will also implement the recommendations from this groundbreaking work as this network formulates a national strategy for young women with breast cancer.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".