Harmonizing evidence-based practice, implementation context, and implementation strategies with user-centered design: a case example in young adult cancer care
Bibliographic record
Abstract
Abstract Background. Attempting to implement evidence-based practices in contexts for which they are not well-suited may compromise their fidelity and effectiveness or burden users (e.g., patients, providers, healthcare organizations) with elaborate strategies intended to force implementation. To improve the fit between evidence-based practices and contexts, implementation science experts have called for methods for adapting evidence-based practices and contexts, and tailoring implementation strategies; yet, methods for considering the dynamic interplay among evidence-based practices, contexts, and implementation strategies remain lacking. We argue that harmonizing the three can be accomplished with User-Centered Design, an iterative and highly stakeholder-engaged set of principles and methods. Methods. This paper presents a case example in which we used User-Centered Design methods and a three-phase User-Centered Design process to design a care coordination intervention for young adults with cancer. Specifically, we used usability testing to redesign an existing evidence-based practice (i.e., patient-reported outcome measure that served as the basis for intervention) to optimize usability and usefulness, an ethnographic user and contextual inquiry to prepare the context (i.e., comprehensive cancer center) to promote receptivity to implementation, and iterative prototyping workshops with a multidisciplinary design team to design the care coordination intervention and anticipate implementation strategies needed to enhance contextual fit. Results. Our User-Centered Design process resulted in the Young Adult Needs Assessment and Service Bridge (NA-SB), including a patient-reported outcome measure redesigned to promote usability and usefulness and a protocol for its implementation. By ensuring NA-SB directly responded to features of users and context, we designed NA-SB for implementation , potentially minimizing the strategies needed to address misalignment that may have otherwise existed. Furthermore, we designed NA-SB for scale-up ; by engaging users from other cancer programs across the country to identify points of contextual variation which would require flexibility in delivery, we created a tool not overly tailored to one unique context. Conclusions. User-Centered Design can help maximize usability and usefulness when designing evidence-based practices, preparing contexts, and informing implementation strategies- in effect, harmonizing evidence-based practices, contexts, and implementation strategies to promote implementation and effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".