Definition of the Concept of Gender Equity and Diversity in Critical Care and Perioperative Settings
Bibliographic record
Abstract
This is an opening chapter of the book where the basic concepts and definitions are introduced, and they are further discussed in the following chapters. The common ground with the terms and targets is set. Difference between equality and equity is explained, concept of intersectionality is underlined, and the need for diversity and inclusion is described. Gender bias and stereotypes have negative effects on all people and communities. They relate to a culture that does not tolerate diversity. Gender equality and equity are not just “technical things”. They will not be solved by simply counting gender at different positions and ticking the boxes. It is a highly political issue that requires a substantial level of social consensus to move forward. This book reflects the need that the challenges which perioperative and critical care medicine share with the global society are recognized and to discuss the ways to address them. However, medical environment has some specific interests that are highlighted in recent years. Sex- and gender-informed medicine is a new paradigm of clinical practice and medical research that considers the association of sex and gender with each element of the disease process from risk to presentation, and to response to therapy. Many gaps in knowledge remain. This book contains a call to action for a culture of change and provision of better healthcare based on fairness, diversity, and inclusion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".