‘Forgotten as first line providers’: The experiences of midwives during the COVID-19 pandemic in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To explore midwives' experiences working on the frontlines of the COVID-19 pandemic in British Columbia, Canada. DESIGN: Qualitative study involving three semi-structured focus groups and four in-depth interviews with midwives. SETTING: The COVID-19 pandemic in British Columbia, Canada from 2020-2021. PARTICIPANTS: 13 midwives working during the first year of the COVID-19 pandemic in British Columbia. FINDINGS: Qualitative analysis surfaced four key themes. First, midwives faced a substantial lack of support during the pandemic. Second, insufficient support was compounded by a lack of recognition. Third, participants felt a strong duty to continue providing high-quality care despite COVID-19 related restrictions and challenges. Lastly, lack of support, increased workloads, and moral distress exacerbated burnout among midwives and raised concerns around the sustainability of their profession. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Lack of effective support for midwives during the initial months of the COVID-19 pandemic exacerbated staffing shortages that existed prior to the pandemic, creating detrimental gaps in essential care for pregnant people, especially with increasing demands for homebirths. Measures to support midwives should combat inequities in the healthcare system, mitigating the risks of disease exposure, burnout, and professional and financial impacts that may have long-lasting implications on the profession. Given the crucial role of midwives in women- and people-centred care and advocacy, protecting midwives and the communities they serve should be prioritized and integrated into pandemic preparedness and response planning to preserve women's health and rights around the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".