Will they always be living the Sisyphus punishment? The triple whammy for racialized women: a qualitative investigation of primary care researchers in Canada
Bibliographic record
Abstract
Background Existing literature overlooks the role of gender and race on research productivity, particularly in the context of primary care research. This study examines how gender and race influence the research productivity of primary care researchers in Canada, addressing a gap in existing literature. Methods Qualitative, descriptive methods were used, involving 60-min interviews with 23 Canadian primary care researchers. 13 participants were female (57%) and 10 participants (43%) were male. Fourteen participants were White (non-racialized; 61%), 8 were racialized (35%) and 1 did not comment on race (4%). Reflexive thematic analysis captured participant perceptions of factors influencing research productivity, including individual, professional, institutional, and systemic aspects. Findings Systemic bias and institutional culture, including racism, sexism, and unconscious biases against racialized women, emerge as key barriers to research productivity. The parenting life stage further compounds these biases. Barriers include lack of representation in faculty roles, toxic work environments, research productivity metrics, and exclusion by colleagues. Participants indicated that institutional reforms and systemic interventions are needed to foster a diverse, equitable, and inclusive environment. Strategies include recruiting equity-focused leaders, increasing representation of racialized female faculty, diversity training, mentorship programs, providing meaningful support, flexible work arrangements, and protected research time. Sponsors can offer more targeted grants for female and racialized researchers. Adjusting metrics for gender, race, parenthood, and collaborative metrics is proposed to enhance diversity and inclusion among researchers. Interpretation This study underscores the importance of addressing systemic bias at institutional and systemic levels to create a fair and supportive environment for primary care researchers. A multitude of strategies are needed including increasing representation of racialized female faculty, creating supportive and psychologically safe work environments, and public reporting of data on faculty composition for accreditation and funding decisions. Together, these strategies can alleviate the triple whammy and free these researchers from the Sisyphus Punishment – the absurdity of being asked to climb a hill while pushing a boulder with no hope of reaching the top. Funding College of Family Physicians of Canada.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.056 | 0.025 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".