Inégalités sociales de santé et rapports de pouvoir : Covid-19 au Québec
Bibliographic record
Abstract
This article proposes to clarify the concept of social inequality in health: theoretically first, then by mobilizing it on a specific study field, the Covid-19 pandemic in Quebec during the spring of 2020.It begins with a discussion of various definitions of social inequalities in health and then proposes the following one: these are differences in health observed between several social groups and which result from the power relation(s) between these groups.Applying this definition to the Covid-19 pandemic occurs in two stages. First, power relations that differentiate exposure to the various risks caused by the pandemic are identified: being infected, dying of it, but also seeing one's health affected by the pandemic without necessarily being infected with the new coronavirus. The study of this latter risk requires monitoring exposure to social determinants of health that is unbalanced by the context of the pandemic: income, social network, care and social services, education, stigma.This first step of the analysis considers power relations taken in isolation from each other. The second explores their articulation. Its common thread is the ethno-racial relation, of which are analyzed the links with socio-economic relation. Finally, a systemic perspective of inequalities is drawn, essential for identifying actions to be taken to fight against social inequalities in health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".