Let us be heard: critical analysis and debate of collaborative research approaches used in implementation science research with equity-deserving populations
Bibliographic record
Abstract
BACKGROUND: Implementation Science research completed with equity-deserving populations is not well understood or explored. The current opioid epidemic challenges healthcare systems to improve existing practices through implementation of evidence-based interventions. Pregnant persons diagnosed with opioid use disorder (OUD) is an equity-deserving population that continues to experience stigmatization within our healthcare system. Efforts are being made to implement novel approaches to care for this population; however, the implementation research continues to leave the voices of pregnant persons unheard, compounding the existing stigma and marginalization experienced. METHODS: This debate paper highlights a specific case that explores the implementation of the Eat, Sleep, Console (ESC) model of care, a function-based empowerment model used to guide the care for pregnant persons diagnosed with OUD and their infants. We establish our debate within the conceptual discussion of Nguyen and colleagues (2020), and critically analyze the collaborative research approaches, engaged scholarship, Mode 2 research, co-production, participatory research and IKT, within the context of engaging equity-deserving populations in research. We completed a literature search in CINAHL, Google Scholar, PubMed and Embase using keywords including collaborative research, engagement, equity-deserving, marginalized populations, birthparents, substance use and opioid use disorder with Boolean operators, to support our debate. DISCUSSION: IKT and Community Based Participatory Action Research (CBPR) were deemed the most aligned approaches within the case, and boast many similarities; however, they are fundamentally distinct. Although CBPR's intentional methods to address social injustices are essential to consider in research with pregnant persons diagnosed with OUD, IKT aligned best within the implementation science inquiry due to its neutral philosophical underpinning and congruent aims in exploring complex implementation science inquiries. A fundamental gap was noted in IKT's intentional considerations to empowerment and equitable engagement of equity-deserving populations in research; therefore, we proposed informing an IKT approach with Edelman's Trauma and Resilience Informed Research Principles and Practice (TRIRPP) Framework.
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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 | 0.675 | 0.725 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.030 | 0.151 |
| Scholarly communication | 0.055 | 0.060 |
| Open science | 0.014 | 0.034 |
| Research integrity | 0.030 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".