A multi-faceted exploration of unmet needs in the continuing improvement and development of fertility care amidst a pandemic
Bibliographic record
Abstract
PURPOSE: The continuous improvement and development of fertility care, internationally, requires ongoing monitoring of current delivery processes and outcomes in clinical practice. This descriptive and exploratory mixed-methods study was conducted in eight countries (Brazil, China, France, Germany, Italy, Mexico, Spain and the United Kingdom) to assess the unmet needs of fertility patients (male and female), and existing challenges, barriers and educational gaps of physicians and laboratory specialists involved in human fertility care during the COVID-19 pandemic. MATERIALS AND METHODS: The study was deployed sequentially in two phases: 1) in-depth 45-minute semi-structured interviews (n=76), transcribed, coded and thematically analysed using an inductive reasoning approach, 2) an online survey (n=303) informed by the findings of the qualitative interviews, face validated by experts in reproductive medicine, and analysed using descriptive and inferential statistical methods. RESULTS: The integrated results of both phases indicated numerous areas of challenges, including: 1) investigating male-related infertility; 2) deciding appropriate treatment for men and selective use of assisted reproductive technology; and 3) maintaining access to high-quality fertility care during a pandemic. CONCLUSIONS: The paper presents a reflective piece on knowledge and skills that warrant ongoing monitoring and improvement amongst reproductive medicine healthcare professionals amidst future pandemics and unanticipated health system disruptions. Moreover, these findings suggest that there is an additional need to better understand the required changes in policies and organizational processes that would facilitate access to andrology services for male infertility and specialized care, as needed.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".