PO55 IDENTIFYING THE EDUCATION, INFORMATION AND SUPPORT NEEDS OF CANADIANS DIAGNOSED WITH BREAST CANCER: A CANADIAN BREAST CANCER NETWORK (CBCN) ASSESSMENT PROJECT
Bibliographic record
Abstract
An online survey LIMBER (LIving with Metastatic Breast CancER) expanded some of the issues that emerged from the ABC2019 patient advocate workshop (Fallowfield et al. 2021) exploring patients' perceptions of the information and support they had experienced.A key emergent theme from the qualitative LIMBER data (n = 144 women) showed that informal caregivers, friends, and family members do not receive sufficient guidance to support patients with MBC.Respondents reported numerous behaviours on the part of friends and family that were both helpful and unhelpful.To help address this we have produced a 25minute information film in which 5 different characters (actors) give voice to quotes direct from the survey.The film is conversational in style and divided into 8 sections: introduction, understanding the diagnosis, dealing with family's emotions and reactions, useful family responses, friends' reactions, useful responses from friends, well-meaning advice, practical help.The quotes are interspersed with an informal discussion between Lesley Stephen, a patient advocate who is living with MBC and Prof Dame Lesley Fallowfield, with a focus on translating the feedback into practical and actionable advice.A draft of the film was shared with 25 members of the public for feedback, usefulness, and acceptability.This was used to produce the final version of the film entitled 'They just don't know what to say or do', which will be made available freely on U-tube and to breast cancer charities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".