Implementation strategies in pediatric emergency management: a scoping review protocol
Bibliographic record
Abstract
Abstract Background : Behaviour change is not simple, and the introduction of guidelines or protocols does not mean that they will be followed. As such, implementation strategies are vital for the uptake and sustainability of changes in medical protocols. Medical or mental emergencies may be fatal, especially in children due to their unique physiological needs. In pediatric emergency settings, where timely decisions are often made, practice change requires thoughtful considerations regarding the best approaches to implementation. As there are many studies reporting on a wide variety of implementation strategies in the pediatric emergency setting, we aim to identify and map their characteristics, especially when successful, in pediatric emergency management (PEM). Methods : We will conduct a scoping review to identify various implementation strategies in PEM using the Arksey and O’Malley framework. We will search Medline (Ovid), Embase (Ovid), Cochrane Central (Wiley) and CINAHL (Ebsco) for implementation studies among the pediatric population (≤21 years) in a pediatric emergency setting. Two pairs of reviewers will independently select studies for inclusion and extract data. We will perform a descriptive, narrative analysis on the characteristics of the identified implementation strategies. Discussion : We will present specific characteristics and outcome measures of all included studies in a tabular form. The results of this review are expected to help identify and characterize successful implementation strategies in PEM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.099 | 0.109 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".