Mother’s Loneliness: Involuntary Separation of Pregnant Women in Maternity Care Settings and Its Effects on the Experience of Mothers during the COVID-19 Pandemic
Bibliographic record
Abstract
The aim of the study was to investigate the challenges of involuntary separation experienced by women during pregnancy and childbirth in the time of the COVID-19 pandemic. The study was conducted by the means of a self-administered questionnaire. One thousand and eleven women (1011) from Poland took part in the study, with an average age of approximately 30 years. The study was approved by the Research Ethics Committee of Warmia and Mazury University in Olsztyn, Poland. The results show that the majority of the surveyed women experienced involuntary separation from their partners during pregnancy and childbirth: 66.27% had no choice but to give birth alone and 84.37% had not been able to attend medical appointments with their partners. Solitary encounters with healthcare were associated with the feeling of fear (36.4%), anger (41%), a sense of injustice (52.2%), acute sadness (36.6%) and a sense of loss (42.6%), with all the reported levels higher in younger women. Over 74% of respondents were afraid of childbirth without a partner present. Almost 70% felt depressed because of a lonely delivery experience. Nearly a quarter of the mothers surveyed declared that if they could go back in time, they would not have made the decision to become pregnant during the pandemic. Based on our study, we found that adjustments to prenatal and neonatal care arrangements under COVID-19-related regimens are needed. Our proposal is to implement at least three fundamental actions: (1) risk calculations for pandemic-related cautionary measures should take into account the benefits of the accompanied medical appointments and births, which should be restored and maintained if plausible; (2) medical personnel should be pre-trained to recognise and respond to the needs of patients as a part of crisis preparedness. If the situation does not allow the patient to stay with her family during important moments of maternity care, other forms of contact, including new technologies, should be used; (3) psychological consultation should be available to all patients and their partners. These solutions should be included in the care plan for pregnant women, taking into account a risk-benefit assessment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".