Equity In Care: Midwifery In Ontario During the COVID-19 Pandemic
Bibliographic record
Abstract
This article explores the impact of the COVID-19 pandemic on midwifery care in Ontario. Midwives faced unique challenges in delivering high-quality care while protecting themselves and their clients from infection during the pandemic. Our first objective in this study was to understand the general impact of the pandemic on midwifery practice to document the challenges midwives faced, and how they adapted their work. What information, resources, and support did they receive to deal with the challenges, and what strategies did they develop to maintain their unique model of care under such constraints? Our second objective was to look closely at how midwives worked to mitigate the pandemic’s unequal burden on racialized and marginalized clients as COVID-19 laid bare and exacerbated existing divides in the healthcare landscape. How did they adapt care for vulnerable groups during a time of crisis? RÉSUMÉLe présent article examine l’incidence de la pandémie de COVID-19 sur les soins sage-femme en Ontario. Les sages-femmes ont affronté des défis exceptionnels : elles devaient offrir des soins de haute qualité tout en protégeant leur clientèle et elles-mêmes contre l’infection. Le premier objectif de notre étude consistait à comprendre l’impact de la pandémie sur la pratique sage-femme en général et à prendre note des défis auxquels les sages-femmes ont fait face et des façons dont elles ont adapté leur travail. Quels renseignements, quelles ressources et quels soutiens ont obtenu les sages-femmes pour relever les défis et quelles stratégies ont-elles conçu pour maintenir le modèle de soins qui leur est propre sous de telles contraintes? Nous avions comme deuxième objectif d’examiner de près la façon dont les sages-femmes ont travaillé pour atténuer le fardeau inégal imposé à la clientèle racisée et marginalisée, alors que la COVID-19 mettait à nu et accentuait les fossés présents dans le paysage des soins de santé. Comment les sages-femmes ont-elles adapté les soins prodigués à ces groupes vulnérables durant cette crise?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".