The psychological impact of the COVID-19 pandemic on fertility care: a qualitative systematic review
Bibliographic record
Abstract
The objective of this systematic review was to characterise psychological impacts of the COVID-19 pandemic related to fertility care. We conducted a systematic search following PRISMA guidelines of five databases (EMBASE, Medline-OVID, CINAHL, Web of Science, and PsycINFO) from March 17th 2020 to April 10th 2021. Citing articles were also hand-searched using Scopus. Of the 296 original citations, we included fifteen studies that encompassed 5,851 patients seeking fertility care. Eleven studies only included female participants, while four included both male and female participants. The fifteen studies unanimously concluded that the COVID-19 pandemic caused negative psychological impacts on fertility care. Risk factors included female sex, single marital state, previous ART failure, prior diagnoses of anxiety or depression, and length of time trying to conceive. Specific concerns included the worry and frustration of clinic closure, concerns about pregnancy and COVID-19 infection, and advancing age. There were contrasting beliefs on whether the decision to stop fertility treatments during the COVID-19 pandemic was justified. In addition, we found that many patients preferred to resume fertility treatment, despite anxieties regarding the risk of the COVID-19 virus. We recommend that fertility providers screen patients for risk factors for poor mental health and tailor support for virtual care.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".