Prioritizing gender equity and intersectionality in Canadian global health institutions and partnerships
Bibliographic record
Abstract
Despite governmental efforts to close the gender gap and global calls including Sustainable Development Goal 5 to promote gender equality, the sobering reality is that gender inequities continue to persist in Canadian global health institutions. Moreover, from health to the economy, security to social protection, COVID-19 has exposed and heightened pre-existing inequities, with women, especially marginalized women, being disproportionately impacted. Women, particularly women who face bias along multiple identity dimensions, continue to be at risk of being excluded or delegitimized as participants in the global health workforce and continue to face barriers in career advancement to leadership, management and governance positions in Canada. These inequities have downstream effects on the policies and programmes, including global health efforts intended to support equitable partnerships with colleagues in low- and middle- income countries. We review current institutional gender inequities in Canadian global health research, policy and practice and by extension, our global partnerships. Informed by this review, we offer four priority actions for institutional leaders and managers to gender-transform Canadian global health institutions to accompany both the immediate response and longer-term recovery efforts of COVID-19. In particular, we call for the need for tracking indicators of gender parity within and across our institutions and in global health research (e.g., representation and participation, pay, promotions, training opportunities, unpaid care work), accountability and progressive action.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".