Canadian Radiology Gender Pay Gap-Reality or Myth?
Bibliographic record
Abstract
Introduction: Prior studies on Canadian physicians’ income have demonstrated a gender pay gap (GPG); however, there is a paucity of data in the Radiology specialty. A cross-sectional study was conducted to determine if practicing Canadian radiologists’ self-reported income is related to gender, controlling for demographic and work variables. Methods: English and French online surveys were distributed by email and social media to radiologists and trainees (May–July 2021). The association between Gender (controlling for Ethnicity variables, Region, having Children, Full-/Part-Time work, and Academic position) and Self-Reported Income was examined using chi-square tests. Pearson correlations examined relationships between opinion variables. Analyses were conducted using SPSS V28.0. A priori significance was P < .05. Study had ethics approval. Results: Four hundred and fifty-four practicing Canadian radiologists responded. Majority were women (51.2%, n = 227), a non-visible Minority (71.7%, n = 317), and from Western Provinces (67.8%, n = 308). Significant relationship was established between Self-Reported Income and Gender (χ2 = 10.44, df = 2, P < .05). More men (70.6%, n = 120) than women (56.4%, n = 110), reported income “greater than $500 000”; fewer men (20.6%, n = 35) than women (35.9%, n = 70) reported “$300 000-$500 000”; a similar percent of men (8.8%, n = 15) and women (7.7%, n = 15) reported “less than $300 000.” No relationship was found between self-reported income and gender for ethnicity variables, those without children, part-time, or non-academic radiologists. The opinion “Addressing the GPG is important” correlated to “Canadian Association of Radiologists should collect demographic data” (r = 0.63). Responses were low for ethnic minorities and non-western provinces. Conclusion: Our results suggest a GPG exists in Canadian radiology and is an important first step for future studies.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".