Longitudinal evaluation of a course to build core competencies in implementation practice
Bibliographic record
Abstract
BACKGROUND: Few training opportunities are available for implementation practitioners; we designed the Practicing Knowledge Translation (PKT) to address this gap. The goal of PKT is to train practitioners to use evidence and apply implementation science in healthcare settings. The aim of this study was to describe PKT and evaluate participant use of implementation science theories, models, and frameworks (TMFs), knowledge, self-efficacy, and satisfaction and feedback on the course. METHODS: PKT was delivered to implementation practitioners between September 2015 and February 2016 through a 3-day workshop, 11 webinars. We assessed PKT using an uncontrolled before and after study design, using convergent parallel mixed methods. The primary outcome was use of TMFs in implementation projects. Secondary outcomes were knowledge and self-efficacy across six core competencies, factors related to each of the outcomes, and satisfaction with the course. Participants completed online surveys and semi-structured interviews at baseline, 3, 6, and 12 months. RESULTS: Participants (n = 15) reported an increase in their use of implementation TMFs (mean = 2.11; estimate = 2.11; standard error (SE) = 0.4; p = 0.03). There was a significant increase in participants' knowledge of developing an evidence-informed, theory-driven program (ETP) (estimate = 4.10; SE = 0.37; p = 0.002); evidence implementation (estimate = 2.68; SE = 0.42; p < 0.001); evaluation (estimate = 4.43; SE = 0.36; p < 0.001); sustainability, scale, and spread (estimate = 2.55; SE = 0.34; p < 0.001); and context assessment (estimate = 3.86; SE = 0.32; p < 0.001). There was a significant increase in participants' self-efficacy in developing an ETP (estimate = 3.81; SE = 0.34; p < 0.001); implementation (estimate = 3.01; SE = 0.36; p < 0.001); evaluation (estimate = 3.83; SE = 0.39; p = 0.002); sustainability, scale, and spread (estimate = 3.06; SE = 0.46; p = 0.003); and context assessment (estimate = 4.05; SE = 0.38; p = 0.016). CONCLUSION: Process and outcome measures collected indicated that PKT participants increased use of, knowledge of, self-efficacy in KT. Our findings highlight the importance of longitudinal evaluations of training initiatives to inform how to build capacity for implementers.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".