Power and privilege in pediatric anesthesia
Bibliographic record
Abstract
No one reading this paper will have any surprise that surgical and anesthesia care for children is better developed in Boston than it is in Bujumbura.The gradients in the availability of safe, timely, and high-quality care are steep.For many of us it may be easy to think of these differences as just "the way it is", or even, not to think about it at all as we go about our day-to-day work of providing the best possible anesthesia care for the children who pass through our own institutions.However, this relatively comfortable perspective may be the result of power and privilege, phenomena notoriously underappreciated by those who benefit from it.In fact, health inequities generally result from the interaction of social determinants of health with fragile healthcare systems and have underlying economic and political causes.The Coin Model of privilege is a useful metaphor.1 The "coin" is a system that promotes inequity.There are two sides of the coin-the same system that gives some people a disadvantage in life that they neither earned (privilege) provides others with advantages that they did not earn nor deserve (often described as oppression).This is simply by an accident of birth, of who that person happens to be.These coins stack, so that some people have privilege in some areas but can be considered to be oppressed in other areas, or may have multiple areas of being underprivileged (sometimes called intersectionality).So where is the power and privilege that affects children who need surgical and anesthesia care globally?What are the "coins" that represent systems that perpetuate inequity? | P OV ERT YHousehold wealth is probably the most important social determinant of the health of children and varies both between and within countries.This influences whether the child access to private versus public healthcare institutions with potential privilege in terms of both material and human resources (Figure 1).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".