Under the microscope: 'race', gender, and medical laboratory science in Canada
Bibliographic record
Abstract
Canada's third largest health profession, medical laboratory science, is a feminized and largely invisible profession in which the practitioners are not permitted to claim the knowledge that they create in their everyday work. My research documents inequitable practices as they can be seen in the experiences of medical laboratory technologists as well as in the institutionalized practices that discount their work. I describe my historical document analysis and survey of practitioners, situating myself as an insider making use of a critical feminist perspective. I address the social and historical foundations of the profession, issues of race and gender in laboratory work, and the implications of these findings for professional change in medical laboratory science. I advocate several means of re-visioning of medical laboratory science to acknowledge the role of medical laboratory technologists in knowledge creation. As well, I suggest that research on the professions encourage inquiry into intersecting racist, sexist and classist practices; that researchers on race enhance their awareness of the potentially disadvantaging assumptions built into certain research practices; and that health policy-makers address the issue of toxic work environments, unvalued health care workers, and inequitable policy-making practices in order to safeguard health practitioners and the quality of patient care. My historical analysis reveals that laboratory work and the division of labour in the laboratory arose within racist, sexist and classist practices of nineteenth-century science and medicine; my discussion lays out the relations of dominance by the medical profession that characterized the health professions throughout the twentieth century. I discuss 'race', gender and medical laboratory science, showing how men's and women's experiences in the profession differ in terms of their educational attainment, career advancement, workplace activities, and participation in part-time and contingency work. The racialized division of labour becomes apparent when laboratory workers above and below medical laboratory technologists are considered. My analysis of the experiences of medical laboratory technologists during health care restructuring demonstrates the impact of reductionist ideologies of cost and efficiency on the lives and work of laboratory practitioners, revealing the shift of control over laboratory work from the medical profession to administrators and corporate interests.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".