Implementation strategies in emergency management of children: A scoping review
Bibliographic record
Abstract
BACKGROUND: Implementation strategies are vital for the uptake of evidence to improve health, healthcare delivery, and decision-making. Medical or mental emergencies may be life-threatening, especially in children, due to their unique physiological needs when presenting in the emergency departments (EDs). Thus, practice change in EDs attending to children requires evidence-informed considerations regarding the best approaches to implementing research evidence. We aimed to identify and map the characteristics of implementation strategies used in the emergency management of children. METHODS: We conducted a scoping review using Arksey and O'Malley's framework. We searched four databases [Medline (Ovid), Embase (Ovid), Cochrane Central (Wiley) and CINAHL (Ebsco)] from inception to May 2019, for implementation studies in children (≤21 years) in emergency settings. Two pairs of reviewers independently selected studies for inclusion and extracted the data. We performed a descriptive analysis of the included studies. RESULTS: We included 87 studies from a total of 9,607 retrieved citations. Most of the studies were before and after study design (n = 68, 61%) conducted in North America (n = 63, 70%); less than one-tenth of the included studies (n = 7, 8%) were randomized controlled trials (RCTs). About one-third of the included studies used a single strategy to improve the uptake of research evidence. Dissemination strategies were more commonly utilized (n = 77, 89%) compared to other implementation strategies; process (n = 47, 54%), integration (n = 49, 56%), and capacity building and scale-up strategies (n = 13, 15%). Studies that adopted capacity building and scale-up as part of the strategies were most effective (100%) compared to dissemination (90%), process (88%) and integration (85%). CONCLUSIONS: Studies on implementation strategies in emergency management of children have mostly been non-randomized studies. This review suggests that 'dissemination' is the most common strategy used, and 'capacity building and scale-up' are the most effective strategies. Higher-quality evidence from randomized-controlled trials is needed to accurately assess the effectiveness of implementation strategies in emergency management of children.
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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 | 0.042 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".