Translational framework for implementation evaluation and research: implementation strategies derived from normalization process theory
Bibliographic record
Abstract
Abstract Background Implementation strategies are deliberate systematic actions used to support the uptake of innovations in health and social care. While widely used taxonomies such as ERIC and EPOC have emerged from consensus exercises, few implementation strategies are explicitly derived from theory and tested against empirical data. This study develops a taxonomy of implementation strategies grounded in Normalization Process Theory (NPT), an implementation theory that explains how new practices become embedded and sustained. Methods We conducted a qualitative evidence synthesis of studies that reported implementation projects informed by NPT. Studies were identified through citation tracking and database searches, screened using pre-specified criteria, and appraised for methodological quality. Using the NPT coding manual, we identified implementation mechanisms described in each study and translated these into candidate implementation strategies. These were then tested against all included studies through iterative qualitative content analysis. Result Searches led to 9,147 references, and we then eliminated 5,708 duplicates. After title and abstract screening a further 1,443 were eliminated. Full text screening was undertaken with 1,996 papers, and 1,411 of these were eliminated. This left 585 papers subjected to quality assessment, of which 522 were eliminated. Finally, 63 papers were included in the review. Qualitative analysis of included papers yielded 24 general strategies linked to NPT’s theoretical constructs and 96 micro-strategies representing four domains of implementation activity: leadership, information, empowerment, and service user involvement. Each strategy was explicitly linked to an NPT construct. Conclusions This study provides a theory-based and empirically grounded set of actionable implementation strategies. These are grounded in qualitative descriptions of implementation work. These strategies support practical decision-making across the planning, delivery, and sustainment phases of implementation, and offer context-sensitive guidance for adapting interventions to diverse settings. Unlike consensus-based taxonomies, these strategies are tied to observable mechanisms of action, enabling users to better understand and respond to the dynamic and socially organised nature of implementation. The NPT taxonomy of implementation strategies can support the design, tailoring, and operationalisation of implementation efforts across varied health and social care 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 | 0.337 | 0.325 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".