Artificial Reproductive Technology Use and Family-Building Experiences of Female Adult Childhood Cancer Survivors: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: Cancer treatments can result in subfertility or infertility in female adult childhood cancer survivors (ACCSs). While ACCSs may utilize assisted reproductive technology (ART) or other family-building options, the limited evidence describing their experiences remains a hindrance to developing and implementing appropriate patient-centered supports. The study's aim is to describe the challenges female ACCSs experienced while navigating ART and family-building options, to inform improvements in clinical practice in a western Canadian province. METHODS: In this qualitative Interpretive Description study, interviews were conducted with 15 female ACCSs and data were analyzed using an interpretive thematic approach and constant comparative techniques. RESULTS: ACCSs' narratives suggest they experienced five prominent challenges while navigating ART and family-building options, including (1) confronting unexpected, impaired fertility, (2) grieving loss and redefining identity, (3) encountering unsupportive healthcare, (4) exploring alternative paths of adoption and international family-building, and (5) facing financial strain. CONCLUSIONS: This exploratory study provides initial insights into the significant and multifaceted challenges female ACCSs experience related to family building and highlights gaps in healthcare services. Further research is warranted to articulate these challenges across contexts and the development and implementation of mitigating approaches. IMPLICATIONS FOR CANCER SURVIVORS: The integration of comprehensive informational, psychosocial, and financial supports into existing cancer survivor and family-building services is vital to meeting female ACCSs' unmet needs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".