Symptom appraisal and help- seeking before a cancer diagnosis during pregnancy: a qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The estimated incidence of a cancer diagnosis during or shortly after pregnancy is 1 in 1000 pregnancies in England. Pregnancy can have an impact on symptom appraisal and help-seeking for symptoms subsequently diagnosed as cancer. Little is known about the pathway to cancer diagnosis in pregnancy or delays that women can encounter. AIM: To explore symptom appraisal, help-seeking decisions, and experience of receiving a cancer diagnosis during pregnancy. DESIGN AND SETTING: Semi-structured interviews were conducted with women diagnosed with cancer during or shortly after pregnancy in the previous 4 years in the UK, recruited between January and May 2022 via the charity Mummy's Star. METHOD: This study used reflexive thematic analysis of 20 interviews. Analysis was largely inductive and the themes generated were mapped onto the intervals of the Model of Pathways to Treatment. RESULTS: Symptoms were often interpreted through the lens of pregnancy by both participants and most of the healthcare professionals from whom they sought help. Participants who found breast lumps were likely to suspect cancer and be referred promptly for tests in secondary care. Although most participants sought timely help for their symptoms, some subsequently encountered health system delays, partly owing to both the vague nature of their symptoms and the COVID-19 pandemic. CONCLUSION: Health services need to better support women presenting with possible cancer symptoms during pregnancy to ensure timely diagnosis. Recommendations include prioritising symptoms over attributing them solely to pregnancy, ensuring timely referrals to rule out serious conditions, and emphasising clear communication alongside robust safety-netting practices. A full assessment is essential before dismissing symptoms as pregnancy related.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it