Patients' preferences for distributing limited government‐funded IVF cycles
Bibliographic record
Abstract
OBJECTIVE: On December 21, 2015, Ontario began funding one cycle of IVF for each resident with a uterus under the age of 43, but with a program cap that is insufficient to meet the annual demand. Our objective was to determine how fertility patients believe that the limited number of funded IVF cycles should be distributed. METHODS: A survey was distributed to patients attending a university affiliated hospital-based fertility clinic in downtown Toronto, including its associated peripheral satellite clinics. RESULTS: From August 2016 to March 2017, 271 patients responded to the survey, of whom 90.3% were in favour of public funding for IVF. The majority of participants favoured allocating IVF cycles to maximize patients' access to IVF in Ontario rather than targeting funded IVF cycles so as to maximize live births (62.7% vs. 32.8%). Most participants wanted all clinics to adopt the same approach for distributing funded IVF cycles compared to the current system in which each clinic chooses its own criteria for allocation (84.5% vs. 8.5%). Participants favoured distributing IVF by way of a scoring system that took individual patient factors into account. However, the factors that each respondent considered important varied materially. CONCLUSION: Patients overwhelmingly supported public funding for IVF, desired a consistent policy for distribution of limited funded IVF cycles at all clinics, and preferred a method that took individual patient factors into consideration when determining patient priority for funded IVF but there were heterogenous opinions on which factors should be included.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".