Systematic development of a set of implementation strategies for transitional care innovations in long-term care
Bibliographic record
Abstract
BACKGROUND: Numerous transitional care innovations (TCIs) are being developed and implemented to optimize care continuity for older persons when transferring between multiple care settings, help meet their care needs, and ultimately improve their quality of life. Although the implementation of TCIs is influenced by contextual factors, the use of effective implementation strategies is largely lacking. Thus, to improve the implementation of TCIs targeting older persons receiving long-term care services, we systematically developed a set of viable strategies selected to address the influencing factors. METHODS: As part of the TRANS-SENIOR research network, a stepwise approach following Implementation Mapping (steps 1 to 3) was applied to select implementation strategies. Building on the findings of previous studies, existing TCIs and factors influencing their implementation were identified. A combination of four taxonomies and overviews of change methods as well as relevant evidence on their effectiveness were used to select the implementation strategies targeting each of the relevant factors. Subsequently, individual consultations with scientific experts were performed for further validation of the process of mapping strategies to implementation factors and for capturing alternative ideas on relevant implementation strategies. RESULTS: Twenty TCIs were identified and 12 influencing factors (mapped to the Consolidated Framework for Implementation Research) were designated as priority factors to be addressed with implementation strategies. A total of 40 strategies were selected. The majority of these target factors at the organizational level, e.g., by using structural redesign, public commitment, changing staffing models, conducting local consensus discussions, and organizational diagnosis and feedback. Strategies at the level of individuals included active learning, belief selection, and guided practice. Each strategy was operationalized into practical applications. CONCLUSIONS: This project developed a set of theory and evidence-based implementation strategies to address the influencing factors, along further tailoring for each context, and enhance the implementation of TCIs in daily practice settings. Such work is critical to advance the use of implementation science methods to implement innovations in long-term care successfully.
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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 | 0.111 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 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".