Patients’ and providers’ perspectives on the decision to undergo non-urgent egg freezing: a needs assessment
Bibliographic record
Abstract
Abstract Background Previous research has demonstrated that patients have difficulty with the decision to undergo non-urgent egg freezing (EF). This study aimed to investigate the decisional difficulties and possible decisional support mechanisms for patients considering EF, and for their providers. Methods This qualitative study involved a needs assessment via individual interviews. Participants included patients considering EF at one academic fertility clinic and providers from across Canada who counsel patients considering EF. 25 participants were included (13 providers and 12 patients). The interview guide was developed according to the Ottawa Decision Support Framework. Interviews were transcribed, and transcripts analyzed for themes and concepts using NVIVO 12. Findings Multiple factors contributing to decisional difficulty were identified, including: (1) multiple reproductive options available with differing views from patients/providers regarding their importance; (2) a decision typically made under the pressure of reproductive aging; (3) uncertainty surrounding the technology/inadequate outcome data; (4) the financial burden of EF; (5) inherent uncertainty relating to potential decision regret; and (6) differing perceptions between patients/providers regarding the role providers should play in the decision. Additionally, potential sources of decisional support were identified, including provision of basic information before and/or during initial consultation, followed by an opportunity during or after initial consultation for clarifying information and helping with value judgements. Individualized counselling based on patient values, adequate follow-up, psychosocial counselling, and peer support were also emphasized. Conclusions More decisional support for women considering EF is needed. Suggestions include a patient decision aid in conjunction with modified healthcare provider counselling, support and follow up.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".