Barriers to the implementation of sepsis guideline in a Canadian pediatric tertiary care centre
Bibliographic record
Abstract
Introduction : Adherence to Clinical Practice Guidelines for sepsis can improve care processes and outcomes; however, sepsis guideline adherence is plagued by many barriers. The purpose of this article is to report the perceived barriers for implementing a sepsis guideline at British Columbia’s Children Hospital. Methods : This is a mixed method study. Data were collected from clinicians using a questionnaire that covered 3 major domains and included two open-ended questions. Quantitative data analysis focused on the Mean Overall Barrier Score (MOBS) in each category using descriptive and inferential statistical techniques. Qualitative data were analyzed thematically through deductive and inductive approaches. Results : A total of 176 clinicians participated in the study. Nurses and physicians were the largest groups of participants (52.7% and 41.2%, respectively). Nurses perceived more barriers (MOBS: 3.3; 95% CI: 3.1-3.4) compared to attending physicians (MOBS: 3.6; 95% CI: 3.3-3.8). The most frequent type of barriers reported was contextual, including environmental and guideline related barriers (MOBS: 3.1; 95% CI: 2.9-3.3), whereas the least barrier reported was lack of motivation (MOBS: 4.0; 95% CI: 3.9-4.2). Clinicians who were highly motivated and perceived less environmental barriers were more likely to use the guideline (Odds Ratio of 2.2 [ p = .036] and 2.2 [ p = .092], respectively). Conclusions : Motivation was the most important predictor of guideline use while contextual barriers hindered use. Therefore, motivating the clinicians and removing external barriers offers the best chance for successful guideline implementation. Furthermore, removing barriers for the use of sepsis guideline among nursing group needs more consideration.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".