Racialized Student Midwives’ Exploration of Resilience in the Ontario Midwifery Education Program: A Qualitative Descriptive Study
Bibliographic record
Abstract
Background: Resilience is often positioned as a response that empowers midwives and midwifery students to cope with challenges and support professional longevity. However, formally documented understandings of resilience do not account for the unique and varying experiences of racialized midwifery students. In order to address this gap, we asked the following research question: “How do racialized midwifery students in Ontario experience resilience?” Methods: We conducted a qualitative descriptive study using focus group interviews. We recruited current and past students who attended the midwifery education program in Ontario via email, social media, and word of mouth. We asked interested participants to complete an online demographic questionnaire via REDCap. Two investigators, a midwifery student and a midwife who both identify as racialized, co-facilitated focus groups that were held in person in Toronto and Hamilton and online. We used REDCap to generate descriptive statistics summarizing the demographic characteristics of participants. One investigator transcribed audio recordings of the focus groups. We managed and analyzed transcripts in NVivo. Two investigators conducted open coding of all the transcripts together. Coding results were then discussed with a third investigator to formulate categories and themes. Key Findings: A total of 16 participants took part in four focus groups in August 2019. Racialized students understand and experience resilience in varying and complex ways. We identified five major themes pertaining to resilience: “defining” resilience, mental toll, active vs. passive coping, individual vs. collective resilience, and agency. Additional themes beyond resilience included systemic exclusion, whiteness, and midwifery culture in the Midwifery Education Program. Implications: Our findings provide specific insights that should be used to guide efforts to improve the experiences of racialized midwifery students throughout their time in the Ontario Midwifery Education Program. This article has been peer reviewed.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".