Transforming healthcare by prioritizing qualitative and quantitative clinical trial evidence: evaluating the Aging, Community and Health Research Unit’s Community Partnership Program for Older Adults (ACHRU-CPP)
Bibliographic record
Abstract
BACKGROUND: This study aimed to test the effectiveness and implementation of a complex integrated care intervention for older adults. We collected both quantitative and qualitative data, which is recommended in evaluating complex interventions to gain a comprehensive understanding of key success factors. Often, congruence is sought and considered desirable when integrating the findings from both data types. However, data are not always congruent, nor is it suboptimal when incongruence occurs, as we illustrate in this case study. We present the divergent findings from a large community-based implementation-effectiveness hybrid type II trial, and how the struggle to reconcile incongruent results yielded rich insights informing the next steps for translational research on the intervention being tested. METHODS: Previous foundational research, including a pilot study and randomized controlled trial (RCT), showed promising results and supported proceeding with a multi-site pragmatic hybrid type II effectiveness-implementation RCT. This recent RCT was undertaken and quantitative and qualitative data were collected to inform the effectiveness and implementation evaluation. To synthesize the findings and guide integration of this large body of evidence, we developed a conceptual model which combined two existing frameworks: the Consolidated Framework for Implementation Research and Quintuple Aim. We used this model to identify the evidence and relate it to relevant implementation and intervention determinants/outcomes. We then synthesized the evidence to distall the main messages regarding the future of the intervention, which involved reconciling apparently discrepant findings from the quantitative and qualitative approaches. RESULTS: The current RCT showed no statistically significant effect for participants for the primary (or secondary) outcomes yet the implementation evaluation consistently found perceived benefits of the intervention for patients, providers, and the healthcare system. Qualitative evidence was critical in understanding contextual factors potentially responsible for the absence of a treatment effect (e.g., COVID-19), strategies to overcome challenges experienced in participant engagement and intervention delivery, and recent policy/practice setting changes which showed strong alignment with the intervention and supported its future implementation. CONCLUSIONS: With the goal of the hybrid type II effectiveness and implementation study in mind, stakeholders encouraged proceeding with a scalability assessment to consider the evidence from the current trial within the context of our prior research, the broader literature for similar interventions, and the ever-changing policy context. TRIAL REGISTRATION: clinicaltrials.gov NCT03664583. Registration date: September 10, 2018.
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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 | 0.664 | 0.650 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".