The impact of COVID-19 on the provision of respectful maternity care: Findings from a global survey of health workers
Bibliographic record
Abstract
BACKGROUND: Significant adjustments to maternity care in response to the COVID-19 pandemic and the direct impacts of COVID-19 can compromise the quality of maternal and newborn care. AIM: To explore how the COVID-19 pandemic negatively affected frontline health workers' ability to provide respectful maternity care globally. METHODS: We conducted a global online survey of health workers to assess the provision of maternal and newborn healthcare during the COVID-19 pandemic. We collected qualitative data between July and December 2020 among a subset of respondents and conducted a qualitative content analysis to explore open-ended responses. FINDINGS: Health workers (n = 1127) from 71 countries participated; and 120 participants from 33 countries provided qualitative data. The COVID-19 pandemic negatively affected the provision of respectful maternity care in multiple ways. Six central themes were identified: less family involvement, reduced emotional and physical support for women, compromised standards of care, increased exposure to medically unjustified caesarean section, and staff overwhelmed by rapidly changing guidelines and enhanced infection prevention measures. Further, respectful care provided to women and newborns with suspected or confirmed COVID-19 infection was severely affected due to health workers' fear of getting infected and measures taken to minimise COVID-19 transmission. DISCUSSION: Multidimensional and contextually-adapted actions are urgently needed to mitigate the impacts of the COVID-19 pandemic on the provision and continued promotion of respectful maternity care globally in the long-term. CONCLUSIONS: The measures taken during the COVID-19 pandemic had the capacity to disrupt the provision of respectful maternity care and therefore the quality of maternity 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".