Theory-guided development of fertility care implementation strategies for adolescents and young adult cancer survivors
Bibliographic record
Abstract
Abstract Background Oncofertility care remains under-implemented across oncology and fertility care settings, with limited tools to scale up effective implementation strategies. Guided by the Consolidated Framework for Implementation Research (CFIR), we aimed to systematically assess factors that influence implementation of oncofertility care and map strategies, particularly electronic health record (EHR)-enabled ones, that fit adult and pediatric oncology care contexts. Methods Using purposeful sampling, we recruited healthcare providers and female adolescent and young adult (AYA) cancer survivors from a comprehensive cancer center and a freestanding children’s hospital. Participants underwent semi-structured interviews and focus groups. Using thematic analysis combining inductive codes with CFIR-based deductive codes, we characterized barriers and facilitators to oncofertility care and implementation strategies. Two coders independently coded each transcript, with a third coder resolving discrepancies by consensus. Results We recruited 19 oncology and fertility providers and 9 AYA survivors. We identified barriers and facilitators to fertility care in the CFIR domains of individual, inner setting, outer setting, and process, allowing us to conceptualize oncofertility care in three necessary stages: screening, referral, and fertility preservation counseling. To fit an adult and a children’s context, five implementation strategies were mapped: needs screen using a best practice advisory, referral order, telehealth fertility counseling, provider audit and feedback, and a provider educational session. All but provider education are facilitated by the EHR system. Conclusions An implementation science approach enabled systematic assessment of oncofertility care and co-design of implementation strategies with stakeholders, providing a theory-based approach and scalable EHR tools to support wider dissemination.
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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 | 0.042 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".