Two generations thrive: Bidirectional collaboration among researchers, practitioners, and parents to promote culturally responsive trauma research, practice, and policy.
Bibliographic record
Abstract
OBJECTIVE: , which aims to prevent the intergenerational transmission of ACEs through improving practices and policies within the health care, education, and child welfare systems. METHOD: Community-based Participatory Research (CBPR) and cultural humility provided a framework and key principles for our collaboration, with an emphasis on critical reflection, mitigating power imbalances, and institutional accountability. Qualitative and quantitative methods were used to evaluate outcomes. We describe our process of building an infrastructure for bidirectional collaboration and key lessons learned to offer a roadmap for researchers, clinicians, and advocates who seek to partner in preventing ACEs and subsequent health inequities. RESULTS: Key lessons learned include: the importance of building and maintaining trust, consistently working to mitigate power imbalances, and the power of bidirectional collaboration to maximize the benefit of research and action for communities traditionally marginalized in research and practice. CONCLUSIONS: Cultural humility and CBPR provide a strong foundation to promote bidirectional collaboration among researchers, practitioners, and parents with lived experience of ACEs. (PsycInfo Database Record (c) 2023 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 | 0.128 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".