Trends in guideline implementation: an updated scoping review
Bibliographic record
Abstract
BACKGROUND: Guidelines aim to support evidence-informed practice but are inconsistently used without implementation strategies. Our prior scoping review revealed that guideline implementation interventions were not selected and tailored based on processes known to enhance guideline uptake and impact. The purpose of this study was to update the prior scoping review. METHODS: We searched MEDLINE, EMBASE, AMED, CINAHL, Scopus, and the Cochrane Database of Systematic Reviews for studies published from 2014 to January 2021 that evaluated guideline implementation interventions. We screened studies in triplicate and extracted data in duplicate. We reported study and intervention characteristics and studies that achieved impact with summary statistics. RESULTS: We included 118 studies that implemented guidelines on 16 clinical topics. With regard to implementation planning, 21% of studies referred to theories or frameworks, 50% pre-identified implementation barriers, and 36% engaged stakeholders in selecting or tailoring interventions. Studies that employed frameworks (n=25) most often used the theoretical domains framework (28%) or social cognitive theory (28%). Those that pre-identified barriers (n=59) most often consulted literature (60%). Those that engaged stakeholders (n=42) most often consulted healthcare professionals (79%). Common interventions included educating professionals about guidelines (44%) and information systems/technology (41%). Most studies employed multi-faceted interventions (75%). A total of 97 (82%) studies achieved impact (improvements in one or more reported outcomes) including 10 (40% of 25) studies that employed frameworks, 28 (47.45% of 59) studies that pre-identified barriers, 22 (52.38% of 42) studies that engaged stakeholders, and 21 (70% of 30) studies that employed single interventions. CONCLUSIONS: Compared to our prior review, this review found that more studies used processes to select and tailor interventions, and a wider array of types of interventions across the Mazza taxonomy. Given that most studies achieved impact, this might reinforce the need for implementation planning. However, even studies that did not plan implementation achieved impact. Similarly, even single interventions achieved impact. Thus, a future systematic review based on this data is warranted to establish if the use of frameworks, barrier identification, stakeholder engagement, and multi-faceted interventions are associated with impact. TRIAL REGISTRATION: The protocol was registered with Open Science Framework ( https://osf.io/4nxpr ) and published in JBI Evidence Synthesis.
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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 | 0.108 | 0.254 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.070 | 0.069 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".