Patients’ perspectives on allocation of publicly funded in vitro fertilization in Ontario: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine consumers' perspectives on fair allocation of publicly funded in vitro fertilization (IVF) in the recently implemented Ontario Fertility Program (OFP). The research questions were as follows: 1) What factors do those who require IVF think are important to consider when distributing funded IVF? and 2) What are the barriers to accessing publicly funded IVF? METHODS: We approached this qualitative study with a social constructivist interpretative framework with grounded theory methodology. Data were obtained via focus group. We recruited participants eligible for the OFP from a tertiary care fertility clinic. Two researchers conducted all interviews, independently reviewed the transcriptions and analyzed the data for open coding, followed by axial coding and then selective coding to determine themes. RESULTS: A total of 13 participants (10 women and 3 men with an average age of 36.4 [range 28-40.7] yr) partook in 4 focus groups. The average duration of infertility was 1.9 (range 0.4-3) years. Three important domains were identified. First, the procedure of distributing funds should be done in a transparent and consistent manner. Second, everyone should have a fair and equal chance to accessing the funds. Participants suggested a combination of first-come, first-served and a scoring system as a method to distribute funds. Lack of communication, associated costs and stress of experiencing infertility were cited as barriers to accessing publicly funded IVF. INTERPRETATION: Ensuring equal and fair access to funds should be prioritized, and information about the process and distribution method to obtain OFP funding should be clearly provided to patients. Transparency, standardization and better communication should be implemented to uphold procedural justice for patients and reduce emotional stress. The findings may be considered by policy-makers to improve the current OFP and when developing similar programs.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".