Ready, set, go!: exploring use of a readiness process to implement pharmacy services
Bibliographic record
Abstract
Abstract Background Readiness is an essential precursor of successful implementation; however, its conceptualization and application has proved elusive. R = MC2 operationalizes readiness for use in practice. The purpose of this study was to (1) describe the application of R = MC2 to assess and build readiness in nine healthcare sites responsible for implementing medication management services and (2) gain insights into the sites’ experience. Methods This mixed methods exploratory study used data collected as part of a process evaluation. Understanding application of the readiness process (Aim 1) involved examining team members’ involvement (who?), readiness challenges and readiness building strategies (what?), strategy execution (how much?), and resulting changes (for what purpose?). To understand the sites’ experience with the R = MC2 system (Aim 2), interviews were conducted with six of the sites to identify facilitators, barriers, and lessons learned. Data sources included a document review (e.g., sites’ action plans), survey results, and interview data. Results Sites included primary care and specialty clinics, pharmacies within health systems, and community pharmacies. Teams consisted of 4–11 members, including a lead pharmacist. The teams’ readiness activities clustered into five broad categories of readiness building strategies (e.g., building the operational infrastructure for service integration). Of the 34 strategies identified across sites, 68% were still in progress after 4 months. Engaging in the readiness process resulted in a number of outputs (e.g., data management systems) and benefits (e.g., an opportunity to ensure alignment of priorities and fit of the intervention). Based on the interviews, facilitators of the readiness process included assistance from a coach, internal support, and access to the readiness tools. Competing priorities and lack of resources, timely decision-making, and the timing of the readiness process were cited as barriers. The importance of service fit, stakeholder engagement, access to a structured approach, and rightsizing the readiness process emerged as lessons learned. Conclusions These findings provide valuable insights into the application of a readiness process. If readiness is to be integrated into routine practice as part of any implementation effort, it is critical to gain a better understanding of its application and value.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".