Closing the diversity and inclusion gaps in francophone public health: a wake-up call
Bibliographic record
Abstract
Is the COVID-19 pandemic ultimately just another episode in the history of our era marked by lack of diversity (in gender, discipline, sector, method, origin and age)? Several analyses have already revealed the exclusion of women, civil society or interdisciplinarity in the fight against the COVID-19 pandemic.1 2 In the world of French-speaking public health, this lack of diversity is not only flagrant but above all historical and structural. The absence of diversity is widely known but never considered as a problem to be solved. It is an open secret, which like others in public health,3 manifests in the fight against the COVID-19 pandemic.4 Our point of view in this editorial is part of a vision of public health in the broadest sense of the term, open to the world and to interdisciplinarity, not limited to epidemiology, biostatistics or even health education and behavioural change. We are part of a holistic public health,5 which is in line with very old proposals for a new public health.6 We want to draw attention to the persisting lack of diversity in francophone public health and to stimulate a collective debate to find solutions. Without diversity, which implies a fundamental renewal of ways of thinking, people, approaches and paradigms,7 our indignation will still be valid when the next pandemic arrives. Taking diversity seriously means recognising the plurality of our contemporary societies so that our public health actions are more adapted and therefore more equitable, effective, and fair. Let us begin by painting a picture of this lack of diversity. We focus on three types of flagship public health institutions: (1) public health authorities and COVID-19 scientific committees (2) public health education and research and (3) public health advocacy groups and societies. To show that our observation persists and …
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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 | 0.071 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.040 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.047 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".