Patients' preferences for distributing limited government‐funded IVF cycles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it