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Record W4400615140 · doi:10.22374/cjmrp.v20i2.44

Racism in Ontario Midwifery: Indigenous, Black and Racialized Midwives and Midwifery Students Unsilenced

2024· article· en· W4400615140 on OpenAlexaboutno aff
Feben Aseffa, L Mehari, Faduma Gure, Lloy Wylie

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRacismObstetricsBlack womenSociologyGender studiesMedicineBiology

Abstract

fetched live from OpenAlex

This article reports on findings from a 2019 online survey titled Experiences of Racism Among Ontario BIPOC Midwives and Students in Midwifery Education and Profession, completed by Ontario midwives and midwifery students who identify as Black, Indigenous, or People of Colour (BIPOC). The survey explored their experiences of racism in both midwifery education and profession. In total, 40 participants consented to participate in the survey, of which 36 completed some or all of the survey; 56% identified as midwives in varying stages of their career, and 45% as students. Of these participants, 86% reported experiencing racism in their work as a midwife, and 87% reported witnessing another midwife or midwifery student being a target of racism. In addition, 61% of participants reported not feeling supported by their practice group when confronted with racism. Over 85% of participants agreed or strongly agreed that racism or fear of racism impacts how they communicate or express themselves, their mental health, and their comfort in working in any community where work is available. To achieve racial equity in the profession, participants recommended raising awareness about racism in the profession, increasing diversity in midwifery, and holding accountable people who commit racist acts and perpetuate racist systems. 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.177
GPT teacher head0.537
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Midwifery Research and PracticeSame topicGlobal Health Workforce IssuesFrench-language works237,207