Lack of Gender Disparity Among Administrative Leaders of Canadian Health Authorities
Bibliographic record
Abstract
Background: Gender distribution within the managing bodies of the Canadian health authorities has not been studied despite their integral role in the health care system. The purpose of this study is to quantify gender differences and to craft a geographic gender analysis of such distribution. Methods: Retrospective data collection of all Canadian health authorities at the provincial, territorial, regional, and first nations levels was conducted. The dependent variable was gender, and other covariates, where applicable, included province/territory, region, leadership position, education (PhD or Master's), honorary degree, and primary occupation. Any member within the executive managing body or board of directors of a Canadian health authority was included, unless their gender could not be determined, in which case they were excluded. Results: Quantitative analysis of the 67 health authorities revealed 1346 individuals with identifiable gender (710 women; 636 men). Thematic distribution showed no significant difference in the gender distribution by provinces/territories (chi square = 14.248; p = 0.28), by leadership position (chi square = 1.88; p = 0.75), by education (chi square = 1.85; p = 0.17), or by primary occupation (chi square = 1.53; p = 0.46). Conclusion: The overall number of females exceeded that of males and there were no gender disparities. Critical analysis of probable causes was discussed. Further studies should be conducted to examine the policies and programs within the Canadian health authorities that successfully tackle the retention, recruitment, and promotion of females.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".