Proposal of the Implementation Theory Selection Model and exemplar application in fall injury prevention
Bibliographic record
Abstract
INTRODUCTION: The use of theories, models and/or frameworks (TMFs) in implementation research and practice is essential for developing useful and testable implementation strategies. Recommendations and tools exist to aid implementation groups in selecting TMFs, but they do not explicitly outline a systematic method for identifying and selecting TMFs. This paper aimed to (1) propose a systematic consensus-based method to select TMFs to support implementation processes, and to (2) demonstrate the use of this novel method in the context of researching the implementation of hip protectors for fracture prevention in long-term care (LTC). MATERIALS & METHODS: We developed a systematic, consensus-based method for selecting TMFs, referred to as the Implementation Theory Selection Model (ITSM). The ITSM comprises five steps: (1) identify potentially relevant TMFs; (2) narrow the pool of TMFs; (3) appraise the relevance of eligible TMFs; (4) prioritize a short-list of TMFs for further, in-depth consideration; and (5) select TMFs through consensus with investigators and research user partners. We operationalized each step of the ITSM through a project investigating determinants of hip protector use and organizational readiness for implementation in a LTC organization in Ontario, Canada. RESULTS: Using the ITSM in our case example, we identified 66 TMFs (Step 1). Of these, 23 met our eligibility criteria (Step 2) and were appraised twice, by five appraisers (Step 3). Six TMFs (Step 4) advanced to the consensus meeting, which was attended by nine investigators and three research users, including two organizational partners and one older adult. Three rounds of voting yielded a tie between the TMFs the group felt would be most appropriate. Research users from our partner LTC organization made the final selection preferring the combination of the Practical, Robust Implementation and Sustainability Model and Consolidated Framework for Implementation Research (Step 5). CONCLUSIONS: The ITSM offers a step-by-step guide for implementation groups to adopt a rigorous, transparent and reproducible method for TMF selection. Although we have demonstrated the feasibility of operationalizing each step of the ITSM in our case example, continued research is needed to evaluate and refine the ITSM to ensure it is appropriate for a wide variety of implementation contexts.
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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 | 0.155 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".