Understanding the Determinants That Influence the Development and Implementation of Evidence‐Based Practice Guidelines During Health Emergencies: An Exploratory Sequential Mixed‐Methods Study Protocol
Bibliographic record
Abstract
ABSTRACT Introduction The COVID‐19 pandemic highlighted the crucial role of evidence‐based practice guidelines (EBPGs) in healthcare systems. Reliable and timely guidelines are essential in health emergencies. This study aims to identify and comprehensively understand the determinants that influence the development and implementation of EBPGs during health emergencies from the perspective of guideline developers and implementers. In this article, we describe the study protocol. Methodology We will conduct an exploratory sequential mixed‐methods study composed of four phases: (I) a qualitative descriptive study to document the experiences of guideline developers and implementers and use this data to generate a list of determinants that influence the development and implementation of EBPG; (II) exploratory integration for survey development and validation using the data of phase I; (III) cross‐sectional study to administer the survey to a large sample and estimate the frequency and perceived impact of those determinants; (IV) interpretation of the results: a combination of qualitative and quantitative findings through the narrative description and joint displays to generate a comprehensive understanding of those determinants. We will explore if results vary across subgroups of interest (participants' roles, gender, and type of organization). We will include people worldwide from five groups participating in an EBPG development and implementation process during the pandemic. Discussion This study will provide insights into the determinants that influence the development and implementation of EBPGs during health emergencies. Its potential impact includes improving future health emergency preparedness, addressing equity gaps, and enhancing guideline development and implementation.
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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 | 0.106 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".