Barriers and facilitators affecting implementation of the Canadian clinical practice guidelines for the diagnosis of acute aortic syndrome
Bibliographic record
Abstract
BACKGROUND: Acute aortic syndrome (AAS) is an uncommon, life-threatening emergency that is frequently misdiagnosed. The 2020 Canadian clinical practice guidelines for the diagnosis of AAS incorporate all available evidence into four key recommendations. In order to facilitate the implementation of these recommendations, a clinical decision aid was created. The objective of this study was to identify barriers and facilitators among physicians prior to implementation of the guideline recommendations in a multicentre step wedge cluster randomized control trial. METHODS: We conducted semi-structured interviews with nine emergency room physicians working at five sites distributed between urban academic and rural settings. We used purposive sampling, contacting physicians until data saturation was reached. Interview questions were designed to understand potential barriers and facilitators to guideline recommendation uptake and use. Responses were analysed according to the Theoretical Domains Framework, and overarching themes describing these barriers and facilitators were identified. RESULTS: Two themes and six subthemes encompassing 13 theoretical domains were identified. These included clinical decision-making support, awareness of the evidence, social factors, expected consequences, ability of physicians to acquire the necessary data and ease of use. A majority of interviewees anticipated that the guideline recommendations would support clinical decision making and more effectively risk-stratify patients. Other facilitators included endorsement of the guidelines by professional organizations and peers. Barriers to implementation include the fact that laboratory testing and knowledge of the rationale for its use in the investigation of AAS were not widespread. The complexity of the clinical decision aid and concerns about test specificity were also identified as potential barriers to use. CONCLUSION: Physicians were amenable to using the AAS guideline recommendations to support clinical decision-making and to reduce resource use. A structured intervention should be developed to address the identified barriers and leverage the facilitators in order to ensure successful implementation. Our findings may have implications for the implementation of other guidelines used in emergency departments.
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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 | 0.057 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".