Making good care essential: The impact of increased obstetric interventions and decreased services during the COVID-19 pandemic
Bibliographic record
Abstract
PROBLEM & BACKGROUND: Since the onset of the COVID-19 pandemic in Canada, policies have been implemented to limit interpersonal contact in clinical and community settings. The impacts of pandemic-related policies on experiences of pregnancy and birth are crucial to investigate and learn from. AIM: To examine the impact of pandemic policy changes on experiences of pregnancy and birth, thereby identifying barriers to good care; to inform understandings of medicalization, care, pregnancy, and subjectivity during times of crisis; and to critically examine the assumptions about pregnancy and birth that are sustained and produced through policy. METHODS: Qualitative descriptive study drawing on 67 in-depth interviews with people who were pregnant and/or gave birth in Canada during the pandemic. The study took a social constructionist standpoint and employed thematic analysis to derive meaning from study data. FINDINGS: The pandemic has resulted in an overall scaling back of perinatal care alongside the heavy use of interventions (e.g., induction of labour, cesarian section) in response to pandemic stresses and uncertainties. Intervention use here is an outcome of negotiation and collaboration between pregnant people and their care providers as they navigate pregnancy and birth in stressful, uncertain conditions. DISCUSSION: Continuity of care throughout pregnancy and postpartum, labour support persons, and non-clinical services and interventions for pain management are all essential components of safe maternal healthcare. However, pandemic perinatal care demonstrates that they are not viewed as such. CONCLUSION: The pandemic has provided an opportunity to restructure Canadian reproductive health care to better support and encourage out-of-hospital births - including midwife-assisted births - for low-risk pregnancies.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".