Navigating infertility treatment in Canada : a qualitative study of the experiences of female adult survivors of childhood cancer
Bibliographic record
Abstract
Background: Cancer and cancer treatments during childhood can affect fertility and reproduction during adulthood. Some female adult childhood cancer survivors (ACCS) who experience fertility-related issues and/or infertility might seek treatment, including artificial reproductive technologies (ART), which may help them to conceive. However, the limited evidence of ACCS’s experience of navigating infertility treatments prevents the designing and tailoring of healthcare services to the unique needs of this growing population, particularly in the Canadian public healthcare context. The study purpose was to understand the challenges female ACCS experience while navigating infertility treatments with the intent of using what was learned to inform clinical practice. Research Methods: In this qualitative, interpretive description study, semi-structured interviews were conducted with six female ACCS (aged 25 - 39 years, completed cancer treatment a mean of 23 years prior, and spent a mean of 2.5 years trying to conceive) who experienced infertility and ART. Transcribed interview data were analyzed using inductive coding, constant comparative techniques and writing of memos and analytic notes. Findings: Prominent challenges described by female ACCS included: 1) feeling a sense of shock when impaired fertility became apparent, 2) experiencing loss and grief with impaired fertility and unsuccessful fertility treatments, 3) enduring an impaired self-identity, particularly in relation to womanhood and femininity, triggered by impaired fertility,4) navigating a healthcare system where fertility-related challenges are not always considered. ACCS’ recommendations and aspirations for an improved healthcare system focused on funding, accessibility of ART in Canada, adoption policies and the lack of infertility-specific information and psychosocial supports. Discussion: The findings from this study suggest that for some ACCS, there can be significant loss and grief and impaired self-identity associated with infertility, pregnancy loss, ART and further efforts to become a parent. Further research to expand upon insights from this study is necessary to lay the foundation for re-envisioning Canadian healthcare that addresses the complexity of infertility treatment for cancer survivors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".