How to know what to know: Information challenges for women in the diagnostic phase of breast cancer
Bibliographic record
Abstract
OBJECTIVE: To explore how women describe efforts to seek, appraise and interpret information during the diagnostic phase of her breast cancer care. METHODS: Qualitative interviews with 35 women with breast cancer across Canada, using audio/video recording. Thematic analysis was used to identify topics important to participants (original results published: www.healthexperiences.ca). Secondary analysis of transcripts to identify how women described information flow, content, and management strategies. RESULTS: Women adopt different strategies to optimize access to information, while acknowledging the negative effects of information overload and lack of relevant information. They propose small steps towards gathering and managing information, and to focus initially on understanding their illness. CONCLUSION: Different strategies can help to ensure that women have the right information, in the right format, at the right time. Some of these strategies include developing guidance on how to 'handle' information, helping healthcare professionals identify patient's information preferences, improving the availability, quality and access to experiential information, and facilitating acces to electronic information that can tailor information. Further research to understand how women handle information can inform strategies to help newly-diagnosed patients navigate available information. PRACTICE IMPLICATIONS: Healthcare professionals can work in partnership with patients to tailor reliable information to support informed decision-making.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".