Being a Gynecologist Doctor Specialized in IVF, a Mother and a Woman at the Time of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic is affecting people around the world with terrible impacts on health, economy, social and psychological aspects and so on, but in this paper, I would like to focus attention on how this situation is worsening the female world. Although men have been shown to have a higher risk factor due to the presence of testosterone, women are severely affected due to: Health Impact • Reduction of life expectancy in terms of delay on necessary oncological checks and consequent progression of related pathologies; • Delay/Stop in taking up the battle against the infertility which can result in potential depression; • A significant increase in fears for both, the woman herself and the incoming creature. Social Impact: The ability to resilience and manages critical situations have always been a female peculiarity but we cannot fail to consider objective data such as: • Reduced chances to save the job position after a severe economic crisis; • Enormous stress for the dual role of mother and worker aggravated by the distance learning (DAD) In this work I will bring to the attention my experience in this emergency period as a gynaecologist doctor specialized in In Vitro Fertilization (IVF); as a mother and as a woman living this situation in the Italian Society.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".