Gender and racialisation of pharmaceutical sector leaders in Canada: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE/DESIGN: Lacking diversity in pharmaceutical leadership positions could contribute to inequities in medicine access. The objective of this cross-sectional study was to determine the gender and racial identities of individuals who hold leadership positions in the Canadian pharmaceutical sector. PARTICIPANTS: We compiled a list of all Canadian governmental bodies, pharmaceutical companies and insurance providers. We identified individuals who were part of the leadership team, including executives and members of the board of directors. PRIMARY OUTCOME MEASURES: The main outcomes of the study were the racialisation and gender of the individuals in leadership positions. The gender and racialisation of an individual were determined by reviewing their name, pronouns and institutional profile through internet searches. Two members of the research team performed the assessment and a third reviewer resolved disagreements. RESULTS: We identified 957 individuals holding leadership positions within the pharmaceutical sector, including 280 drug evaluation committee members, 12 governmental executive officers, 273 insurance company executive and board members and 392 executive and board members. Reviewers identified a total of 375 (39.2% of 957) women holding leadership roles, with most of these positions being held by governmental leaders (52.4% of 292) and a minority by insurance (37.0% of 273) and pharmaceutical (30.9% of 392) leaders. There were a total of 157 (16.4% of 957) racialised leaders, with most of these positions being held by governmental (18.5% of 292) and pharmaceutical (18.1% of 392) leaders, and a minority in insurance companies (11.7% of 273). Across the pharmaceutical sector, there were a total of 48 (5.0% of 957) racialised women and 327 (34.2% of 957) white women. CONCLUSIONS: Leaders within the Canadian pharmaceutical sector are mostly white men, and racialised women hold few leadership roles. Public policy should recognise that these institutions are mostly led by white men and reasons for this disparity could be explored.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".