Microaggressions in anesthesiology and critical care: individual and institutional approaches to change
Bibliographic record
Abstract
Microaggressions are subtle verbal or nonverbal insults that convey derogatory and negative messages to and about people who belong to oppressed groups. Microaggressions reflect structurally and historically perpetuated societal values, which advantage some groups of people by considering them to be inherently more worthy than others, while simultaneously disadvantaging others. While microaggressions may seem innocuous and are often unintentional, they cause tangible harm. Microaggressions are commonly experienced by physicians and learners working in perioperative and critical care contexts and are often not adequately addressed, for a multitude of reasons, including witnesses not knowing how to respond. In this narrative review, we provide examples of microaggressions towards physicians and learners working in anesthesia and critical care, and offer individual and institutional approaches to managing such incidents. Concepts of privilege and power are introduced to ground interpersonal interventions within the larger context of systemic discrimination, and to encourage anesthesia and critical care physicians to contribute to systemic solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".