Implementation of an Iraqi women’s behavioral health support group: Lessons learned in a pre and post US election climate
Bibliographic record
Abstract
The Colorado Refugee Wellness center is an integrated primary and behavioral health care provider for newly arrived refugees resettling in the City of Aurora, CO. In the Spring of 2016, behavioral health staff responded to a need identified by Iraqi Muslim women clients to come together to share experiences and get support in light of the rise in anti-Muslim and anti-refugee rhetoric, and so a therapy group for Iraqi women was created.Topics addressed in group included cultural considerations related to communication styles, generational hierarchy, respect of authority, religious values, physical health and building community as well as psychological issues of losses, cultural conflicts, fear and safety, and achieving health goals in the face of adversity. The Iraqi patient navigator served as an invaluable cultural broker in the recruitment and retention of clients, as well as provided cultural insight into conducting ongoing program evaluation to revise the goals and purpose of the group.In this presentation, we share clinical and cross-cultural experiences, as well as lessons learned on the creation, implementation, and adaption of an Iraqi women’s behavioral health support group in the political climate of the 2016 US election year and approaches to flexible program development and evaluation. We discuss the limitations of applicability of Western core group therapy interventions and recovery models based on the insights from the Iraqi women’s group. In particular, we focus on the crisis debrief session with group members on the day following the election via a process of reciprocal healing.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".