Symptom appraisal and help- seeking before a cancer diagnosis during pregnancy: a qualitative study
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.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".