Information Needs About Cancer Treatment, Fertility, and Pregnancy: Qualitative Descriptive Study of Reddit Threads
Bibliographic record
Abstract
BACKGROUND: A reproductive health implication of the increasing incidence of cancer among women is the impact of cancer treatment on fertility. OBJECTIVE: As patients are increasingly using the internet, particularly online forums, to seek and share experiences, our objective was to understand information needs about cancer treatment, fertility, and pregnancy of women with cancer as well as their caregivers. METHODS: We searched threads (original posts and responses) on four subreddit sites of Reddit ("r/Cancer," "r/TryingForABaby," "r/BabyBumps," and "r/Infertility") over a 5-year period between February 4th, 2014 and February 4th, 2019. Threads with original posts involving a lived experience or question regarding cancer treatment and female fertility and/or pregnancy or parenting/having children from the perspective of either patient or caregiver were included in our analysis. We analyzed threads using thematic analysis. RESULTS: From 963 Reddit threads identified, 69 were analyzed, including 56 with original posts by women with cancer and 13 with original posts by caregivers. From threads made by patients, we identified themes on becoming a part of an online community, impacts of cancer treatment and fertility concerns on self and social relationships, making family planning decisions, and experiences with medical team. We also identified a theme on the impact of cancer treatment and fertility concerns on caregivers. CONCLUSIONS: Reddit provided a rich pool of data for analyzing the information needs of women facing cancer. Our findings demonstrate the far-reaching impacts of cancer treatment and fertility on physical, mental, and psychosocial health for both patients and their caregivers.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".