Why women delay seeking assistance for locally advanced breast cancer
Bibliographic record
Abstract
Locally advanced breast cancer (LABC) occurs in 10% to 30% of all new primary breast cancer diagnoses. For reasons that are not well-understood, 20% to 30% of women with breast cancer (at all stages) wait eight weeks or more from the time they notice the breast symptom(s) to when they seek assistance from a health care provider. Comprehending the mechanisms that support the health appraisal and health seeking behaviour is paramount and not well understood, particularly for women with LABC. The purpose of this study was to qualitatively explore the cognitive, emotional, and contextual experience of women with locally advanced breast cancer in the time between symptom discovery and seeking health care. In-depth qualitative interviews and a cross-case, content analysis revealed that the participants delayed their search for assistance because they did not perceive themselves to be at risk for developing breast cancer, had a symptom other than a lump, and so misattributed or minimized the gravity of the symptom, had a previous experience with a benign tumour, and/or were experiencing concurrent stressful life events. These participants were motivated to seek attention if they had an already scheduled appointment with a health professional for another matter, felt the symptom worsen over time, or were motivated by a loved one to seek help. Implications for health promotion and for education with women with benign breast disease, as well as the general public, are discussed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".