Cultural Sources of Strength and Resilience: A Case Study of Holistic Wellness Boxes for COVID-19 Response in Indigenous Communities
Bibliographic record
Abstract
The COVID-19 pandemic has had disproportionately severe impacts on Indigenous peoples in the United States compared to non-Indigenous populations. In addition to the threat of viral infection, COVID-19 poses increased risk for psychosocial stress that may widen already existing physical, mental, and behavioral health inequities experienced by Indigenous communities. In recognition of the impact of COVID-19 related psychosocial stressors on our tribal community partners, the Johns Hopkins Center for American Indian Health Great Lakes Hub began sending holistic wellness boxes to our community partners in 11 tribal communities in the Midwestern United States and Canada in summer of 2020. Designed specifically to draw on culturally relevant sources of strength and resilience, these boxes contained a variety of items to support mental, emotional, cultural, and physical wellbeing. Feedback from recipients suggest that these wellness boxes provided a unique form of COVID-19 relief. Additional Johns Hopkins Center for American Indian Health offices have begun to adapt wellness boxes for the cultural context of their regions. This case study describes the conceptualization, creation, and contents of these wellness boxes and orients this intervention within a reflection on foundations of community-based participatory research, holistic relief, and drawing on cultural strengths in responding to COVID-19.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Case study of a COVID-19 wellness box intervention in Indigenous communities; it invokes community-based participatory research principles as framing, but the object is the intervention.
This describes a wellness-box intervention for Indigenous communities rather than studying research itself.
Case study of Indigenous community wellness boxes during COVID; public health intervention, not research as object.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".