Facilitators and obstacles to sperm banking in young men receiving gonadotoxic chemotherapy for cancer: the perspective of survivors and health care professionals
Bibliographic record
Abstract
BACKGROUND: Testicular cancer and Hodgkin's disease are among the most common malignancies to affect young men of reproductive age. Although both are associated with high rates of infertility, sperm banking (SB) remains underutilized by both diagnostic groups. Reasons for this remain elusive. METHODS: This study used a qualitative design. In-depth interviews were conducted with 20 cancer survivors and 18 health care professionals (HCPs) to examine their perspectives on factors that facilitate or hinder SB. Interview data were analysed using a mixed approach and a three-step process of data reduction, data display and conclusion drawing and verification. RESULTS: Eight factors were identified as having an impact on SB, and findings suggest that effective promotion of SB involves adequate communication around the severity and personal risk for infertility, assessing the importance of patients place on having children, emphasizing the benefits of SB and addressing possible obstacles such as cost, misperceptions or cultural and other factors. In addition, the communicator should be perceived as appealing. CONCLUSIONS: These results are conceptually consistent with both the Health Belief Model and the Elaboration Likelihood Model of health promotion and are useful in informing HCPs on how to better promote SB.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".