“The straw that broke the camel’s back”: An analysis of racialized women clinicians’ experiences providing diabetes care
Bibliographic record
Abstract
INTRODUCTION: Racialized women clinicians (RWCs) experience the brunt of unfair racial and gendered expectations, which is a direct result of their visible identity. Our study sought to understand how these experiences intersect to impact the personal and professional well-being of RWCs, and their approach to diabetes care. METHODS: Data were collected from 24 RWCs working within Canadian diabetes care settings, who participated in semi-structured, one-on-one interviews conducted from April 2021 to September 2021. The data were qualitatively analyzed using thematic analysis to develop emergent themes, and interactions were explored using the socioecological model (SEM), adapted to our study context. RESULTS: We identified three themes: (1) Discordance between self-identity and relational identity impacted how RWCs interacted with others, and how others interacted with them; (2) Tokenistic, "inclusive" organizational policies/practices and inherently racist and sexist social norms permitted acts of discrimination and led to the systematic othering and exclusion of RWCs within the workplace; and (3) Differential treatment of RWCs had both positive and negative impacts on participants' relational, workplace and self-identity. Using the SEM, we also found that differential treatment of RWCs stems from upstream policies, structures, and social norms, percolating through different levels of the SEM, including work environments and communities, which eventually impacts one's relational identity, as well as one's perception of oneself. CONCLUSION: The differential treatment of RWCs arises predominantly from macro systems of the work environment. The burden to address these disparities must be shifted to the source (i.e., namely systems) by implementing interventions that equitably value diversity efforts, institute policies of accountability and correction of implicit biases, and prioritize an inclusive culture broadly across faculty and leadership.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".