Identifying the education, information and support needs of Canadians diagnosed with breast cancer: A Canadian Breast Cancer Network (CBCN) assessment project.
Bibliographic record
Abstract
e13088 Background: Assessing the education, information and support needs of Canadians diagnosed with breast cancer involves categorizing and tailoring topics and methods of delivery but, most importantly, it must be rooted in an understating of the patient. In 2022, CBCN initiated a project to identify the needs of the Canadian breast cancer population and develop tailored, focused programs and materials. Methods: CBCN conducted a series of 45-minute key informant interviews (7) with patients and oncologists to determine needs, gaps, programs and materials. Five 90-minute patient focus groups (32 participants) were conducted to enhance the interviews. One group was specifically for metastatic patients. Findings from the interviews and focus groups were used to inform the questions for a survey which was fielded online May1/June 10, 2022. The data analysis began in September 2022 and is on-going. Results: A review of Canadian triple negative breast cancer (TNBC) patients’ responses (n=185 or 18.9% of respondents) showed that only 29.4% had heard the term TNBC before. When asked about educational resources on clinical, practical and support topics TNBC patients preferred them tailored to their breast cancer type (94.3%). TNBC patients reported difficulty locating resources (74.0%) and rated those available satisfactory to poor (44.6%). Sources included websites, social media and printed materials. Specific topics of interest were new treatments and latest research (66.2%), fear of recurrence (63.1%), prevention of illness after breast cancer (60.1%) and long term effects of treatment (57.4%). Conclusions: Public awareness of the types of breast cancer could promote a better understanding of the nature of the disease. Availability of multiple communication channels provides opportunities to disseminate and update resources and deliver them to the patients and providers. While information exists, identifying and increasing the availability of credible, reliable resources tailored to specific types of breast cancer that are easily accessible to both patients and clinicians could enhance the physician-patient dialogue, potentially improve adherence, promote trust and possibly contribute to improved outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".