Barriers and factors associated with significant delays to initial consultation and treatment for infertile patients and partners of infertile patients
Bibliographic record
Abstract
RESEARCH QUESTION: What are the key drivers and barriers for infertile patients and their partners to see an infertility specialist and initiate treatment? DESIGN: An online, international, 30-minute quantitative survey collected data from 1944 respondents from nine countries. Respondents were infertile patients (n = 1037) or partners of infertile patients (n = 907; but not necessarily partners of the patient sample), at different stages of the treatment journey. RESULTS: The overall average times were 3.2 years to receiving a medical infertility diagnosis, 2.0 years attempting to achieve pregnancy without assistance before treatment, and 1.6 years of treatment before successful respondents achieved pregnancy. The most common driver for considering treatment after a consultation (n = 1025) was an equal desire within the couple to have a child (40.8%). Of the partners (n = 356), 29.8% reported that transparency of information from healthcare professionals about treatment expectations was important. A significantly higher proportion of respondents seeking treatment reported that healthcare professionals offered supportive services (61.2%) and mental health services (62.0%), than of the 207 respondents who did not seek treatment (32.4% and 36.7%, respectively; P < 0.001). Perceived cost was the most commonly reported barrier for respondents not seeking a consultation (37.5% of n = 352) or treatment (42.0% of n = 207). Of the 95 respondents who discontinued treatment, 34.7% discontinued due to the financial impact. CONCLUSIONS: Respondents reported significant delays to seeking treatment, probably negatively impacting the chances of achieving pregnancy. Motivational coherence within couples was a key driver and cost of treatment was the main barrier. Reported supportive service offerings by healthcare professionals were significantly associated with continuation of the treatment journey.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".