A75 IMPLEMENTING A CIRRHOSIS ORDER SET: A QUALITATIVE ANALYSIS OF PROVIDER-IDENTIFIED BARRIERS AND FACILITATORS
Bibliographic record
Abstract
Abstract Background Cirrhosis is the leading cause of mortality and morbidity in individuals with gastrointestinal disease. Multiple care gaps exist for hospitalized patients with cirrhosis, resulting in high rates of re-hospitalization (e.g. 44% at 90 days in Alberta). The Cirrhosis Care Alberta (CCAB) is a 4-year multi-component pragmatic trial with an aim to reduce acute-care utilization by implementing an electronic order set and supporting education across eight hospital sites in Alberta. Aims As part of the pre-implementation evaluation, this qualitative study analyzed data from provider focus groups to identify barriers and facilitators to implementation. Methods We conducted focus groups at eight hospital sites with a total of 54 healthcare providers (3–12 per site). A semi-structured interview guide based upon constructs of the Consolidated Framework for Implementation Research (CFIR) and Normalization Process Theory (NPT) frameworks was used to guide the focus groups. Focus groups were recorded and transcribed verbatim. Data was analyzed thematically and inductively. Results Five major themes emerged across all eight sites: (i) understanding past implementation experiences, (ii) resource challenges, (iii) competing priorities among healthcare providers, (iv) system challenges, and (v) urban versus rural differences. Site-specific barriers included perceived lack of patient flow, time restraints, and concerns about the quality and quantity of past implementation interventions. Facilitators included passionate project champions, and an ample feedback process. Conclusions Focus groups were useful for identifying pre-implementation barriers and facilitators of an electronic orders set. Findings from this study are being refined to address the influence of COVID-19, and the data will be used to inform the intervention roll-out at each of the sites. Funding Agencies Alberta Innovates
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".