Pre-implementation Stage Research to Guide Trauma-Informed Care for Youth With HIV in the Southern US: A Multimethod Study
Bibliographic record
Abstract
BackgroundYouth with HIV disproportionately experience psychological trauma, but implementation methods for trauma-informed care are lacking.MethodsTo identify potential processes and determinants of trauma-informed care implementation, we conducted process mapping and qualitative interviews and thematically applied the organizational trauma resilience framework to elicit perceived safety, stability, and nurturance in a pediatric HIV clinic.ResultsForty-three personnel and 8 patient representatives engaged in process mapping; 20 completed qualitative interviews. Clinic culture was described as supportive, cohesive, and equity-focused, but requiring workflow improvements for patient autonomy. Trauma screening, assessment, and interventions were limited/inconsistently applied, with duplicative risk assessments. Support for professional quality of life was limited, despite burnout/attrition reports. Some personnel had trauma-focused training, but ongoing education and culturally responsive policies were needed.ConclusionsProcess mapping presented as a low-burden tool for unveiling gaps and care standards; alongside qualitative interviews, these methods provided practical insights for trauma-informed HIV care.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".