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Record W3034902319 · doi:10.1186/s43058-020-00036-2

Ready, set, go!: exploring use of a readiness process to implement pharmacy services

2020· article· en· W3034902319 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueImplementation Science Communications · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersConcordia University
KeywordsPharmacyProcess (computing)Process managementHealth careConceptualizationService (business)PharmacistKnowledge managementMedical educationSet (abstract data type)PsychologyNursingMedicineBusinessComputer scienceMarketingPolitical science

Abstract

fetched live from OpenAlex

Abstract Background Readiness is an essential precursor of successful implementation; however, its conceptualization and application has proved elusive. R = MC 2 operationalizes readiness for use in practice. The purpose of this study was to (1) describe the application of R = MC 2 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 = MC 2 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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0030.000
Scholarly communication0.0000.003
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.923
GPT teacher head0.771
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it