Transitioning toward a better future in behavioral health: From institutional betrayal to racial healing.
Bibliographic record
Abstract
Racial trauma, betrayal trauma, and institutional betrayal are baked into the history of the United States. This legacy of injustice encompasses direct experiences of racism and discrimination, as well as a broader sense of betrayal in which the very institutions tasked with helping people, such as the U.S. health care system, let them down in egregious, painful, or even fatal ways. Using the theories of racial trauma (i.e., an extreme stress response to cumulative firsthand and vicarious experiences of racism and discrimination), betrayal trauma (i.e., a violation of trust by a person on whom someone is reliant, particularly for well-being), and institutional betrayal (i.e., when the violation of trust is perpetrated by an institution on which someone relies), we describe how Black Americans have been mistreated by the U.S. health care system in the past and at present. We then utilize a modified version of Bronfenbrenner's socioecological framework to explore the role of racial and betrayal trauma in perpetuating harm against Black Americans at the micro, meso, and macrolevels. We also provide concrete examples and strategies to address racial healing and provide racial trauma-informed care to Black Americans by promoting the role of providers in engaging in such practices, the role of administrators in making these practices part of institutional policy, and the role of broader institutions in admitting wrongdoing and promoting full transparency in making meaningful and lasting changes. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".