Determinants of complexity in clinical practice guidelines: a Delphi study including perspectives from guideline developers and implementers
Bibliographic record
Abstract
ABSTRACT: The Medical Research Council proposed a framework to develop and implement complex interventions in practice. How to adopt these interventions is described in recommendations of evidence-based clinical practice guidelines. Many factors may influence the complexity of a guideline. The aim of this paper is to describe the determinants of complexity in the development and implementation of an evidence-based clinical practice guideline.A working group with 16 participants was established, consisting of a debate team and a Delphi panel. The debate team discussed online to define the key elements of the MRC's definition of a complex intervention to see whether these elements are applicable to guidelines. These elements were presented to the Delphi panel to assess their relevance.After the first round, consensus was reached on eight elements, with the inter-rater reliability varying from 0.83 to 1.00. After the second Delphi round, consensus was reached on two more elements. The consensus stated that these ten elements all define an aspect of the complexity in guidelines. There was no agreement regarding the exclusion of a specific element.Developers and end-users consider that the complexity of a guideline and its implementation is affected when the number of components, settings, targeted behaviors, and stakeholders increase; when a gap exists between the guideline and the reality of clinical practice; or when differences in education are evident between end-users. Moreover, the level of collaboration required of the different end-users, the scope of change, the level of evidence in the guideline, and the workload for end-users also determine complexity. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A333.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".