Exploring Co-production as an Implementation Strategy for Trauma-Informed Care in a Youth-Focused HIV Clinic in Memphis, Tennessee: Mixed Methods Research
Bibliographic record
Abstract
Background: Memphis, Tennessee is second in the nation for HIV incidence, with one in three diagnoses among youth. Psychological trauma disproportionately impacts youth with HIV, compared with HIV-negative counterparts, requiring community-led and trauma-informed solutions to address mental wellness among youth with HIV. However, a dearth of research concentrates on trauma-informed care (TIC) for this population, with little exploration among youth-centered HIV care settings or into strategies for mobilizing communities to develop solutions. Research co-production, an approach in which research beneficiaries engage in research as cooperative partners, aligns with the TIC focus on collaborative decision-making and could be an effective strategy for facilitating collaborative TIC adoption, but formative research is needed to explore this potential. Objective: We sought to explore TIC implementation determinants and contextual factors that might influence research co-production as a strategy for implementation, including appetite for evidence-based approaches, support for co-production, and resources for capacity building. Methods: We applied an exploratory sequential mixed methods design to identify potential barriers and facilitators to TIC implementation in a youth-focused clinic and contextual factors relative to co-production. All clinic personnel were purposively invited to complete semistructured interviews. Thematic analysis, via four cycles of coding, was applied using the Consolidated Framework for Implementation Research 2.0 to qualitative data. Subsequently, a steering committee of clinic personnel was invited to complete surveys, applying the Research Quality Plus for Co-Production framework to explore co-production factors. A deliberative dialog approach was applied to analyze these findings and synthesize them with Consolidated Framework for Implementation Research. Results: A total of 20 personnel completed interviews, and 9 completed surveys. Potential facilitators included perceived clinic cohesiveness, equity focus, and prioritization or compatibility of TIC. Potential barriers included perceived disconnect between the clinic and larger hospital (in which youth with HIV were seen as stigmatized in other areas of the hospital), sustainability concerns related to a perceived lack of championing by leaders, insufficient mental health protocols, a lack of formal patient feedback procedures, and a lack of protected time for personnel activity engagement. Survey responses suggested that the clinic is likely supportive of evidence-based approaches (mean 3.6, SD 0.70) and collaborative research (mean 3.1, SD 0.31) and empowers personnel to participate (mean 3.1, SD 0.22). Conducive to co-production, the environment was seen as learning-centered, where evidence and standardized or validated approaches are prioritized, and there is an openness for innovation, with a focus on health disparities and quality improvement. Potential barriers included change-resistant staff, role silos, and underutilization of staff skills, coupled with a lack of formal research training and time constraints. Conclusions: Findings suggested that TIC implementation is likely to be embraced in the clinic, with co-production perceived as useful and fitting. However, greater effort is needed to integrate patient experiences and test co-production as a TIC implementation strategy.
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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 | 0.096 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".