When it counts the most: Trauma-informed care and the COVID-19 global pandemic
Bibliographic record
Abstract
CONTEXT: Evidence from the COVID-19 crisis suggests that children and youth are more likely to be subjected to maltreatment and exposure to family violence, while experiencing limited access to the usual services that support vulnerable families and provide targeted services to meet their needs. The current global pandemic itself can also be experienced as a traumatic event. Trauma-informed care draws attention to the potential impacts, from the individual to the global, that myriad traumatic experiences can illicit and proposes using these understandings as foundational to the development and implementation of policy and practice. OBJECTIVE: The aim of this opinion paper is to offer insights to guide practices and policies during this unprecedented global crisis through a discussion of the Substance Abuse and Mental Health Services Administration (2014)’s six trauma-informed care principles: trustworthiness and transparency; safety; peer support; collaboration and mutuality; empowerment and choice; and cultural, historical and gender issues. FINDINGS: Specific recommendations based on these six principles and applied to the current situation are presented and discussed. These principles can serve both in the immediate crisis and as preventative measures against unforeseen future traumatic contexts. CONCLUSION: COVID-19 renews the imperative to maintain and strengthen trauma-informed practices and policies. We argue that never before has trauma-informed care been so important to promote the health and well-being of all and to protect our marginalized populations at greatest risk.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.008 | 0.016 |
| 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".