Improving the sustainability of advance care planning for older adults with serious chronic illnesses in primary care: protocol for a mixed-methods process evaluation of a tailored multifaceted knowledge translation intervention
Bibliographic record
Abstract
Abstract Background: Implementation scientists and practitioners, alike, recognize the importance of sustaining practice change, however post-implementation studies of evidence-based practice interventions are rare. This protocol describes the methodology for a post-implementation research project that will add value to a recently completed randomized controlled trial study comparing the effectiveness of team-based to clinician-focused models for implementing a recognized advance care planning (ACP) training program called SICP (The Serious Illness Care Program). SICP has been implemented in 40 primary care practices recruited from seven Practice-Based Research Networks (PBRNs) in the US and Canada. The aim of this study is to contribute to knowledge on optimal strategies to improve the sustainability of ACP use among healthcare professionals (HPs) providing care to older adults with serious chronic illnesses in primary care and factors that influence ACP sustainability. Methods: The specific objectives of this study will be accomplished using a mixed methods sequential explanatory design nested within a five-phase process evaluation guided by Intersectionality Theory and the Integrated Sustainability Framework. Phase 1 of the study will involve a user-centered design (UCD) approach and qualitative methods to explore barriers and facilitators to ACP sustainability with knowledge users (KUs), which will be mapped to theoretically informed strategies for improving ACP sustainability. Using results from Phase 1, Phase 2 will follow an Evidence-Based Quality Improvement (EBQI) process engaging KUs to identify potentially effective sustainability strategies for ACP use that match priorities/needs and co-design an ACP-focused sustainability intervention that will be tailored to the primary care context. Phase 3 will comprise a quasi-experiment involving up to eight primary care practices with one experimental group of an ACP-focused sustainability intervention and one wait-list control group. The primary outcome will include measures that ACP has successfully been used by primary care HPs, by conducting a medical chart review of eligible older patients with serious chronic illnesses over a 12-month period. Secondary outcomes will include additional provider-level measures such as HPs’ beliefs about ACP, self-efficacy to ACP use, and perceived acceptability of ACP for patients. Phase 4 will use interpretive description and individual semi-structured interviews with a subsample of HPs and local managers who participated in Phase 3 to explore factors influencing ACP sustainability. Finally, quantitative and qualitative results from Phases 3 and 4 will be integrated in Phase 5 to uncover key conditions for improving ACP sustainability in primary care, using a joint display technique. Discussion: This project strives to advance knowledge on optimal strategies for sustainable practice changes introduced through the implementation of evidence-based practice interventions and to deepen our understanding of the factors that influence sustainability. Our results will inform KUs (e.g., patients, clinicians, managers, policymakers) regarding the sustainability of knowledge translation (KT) interventions for ACP. An integrated KT plan of our results will be tailored to end-users and include passive (e.g., publications, website posting) and interactive (e.g., social media, knowledge exchange events with stakeholders) strategies.
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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 | 0.095 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".