What women want to hear: the helpful and unhelpful comments reported by women struggling with infertility amidst the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Many women struggling with infertility report that they frequently experience unhelpful social interactions with well-meaning loved ones and healthcare providers, contributing to a reluctance to confide in others about their infertility and emotional distress. However, it remains unclear what interaction content women experience as 'helpful' versus 'unhelpful,' making it difficult to provide concrete recommendations to the public about how best to support individuals struggling with infertility. METHODOLOGY: Eighty women from Canada and the United States (ages 20-45 years) whose fertility treatments had been cancelled due to the COVID-19 pandemic were recruited via social media to complete an online survey, which included two open-ended questions about the most helpful and unhelpful social interactions they had had about their infertility. Two independent researchers conducted content analysis to identify categories of helpful and unhelpful social interactions. RESULTS: The following six categories were identified by women as helpful: 1) Listening, 2) Fostering hope, 3) Talking to individuals with lived experience, 4) Distraction, 5) Validating emotions, and 6) Tangible support. Responses about unsupportive interactions fell into four categories: 1) Toxic positivity, 2) Advice-giving, 3) Invalidation, and 4) Intruding. Sample quotes from each category are provided. CONCLUSION: These findings provide valuable insights that can be used to develop future educational materials for the general public on how to interact with individuals experiencing infertility.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".