Designing a Behaviour Change Wheel guided implementation strategy for a hypoxaemic respiratory failure and ARDS care pathway that targets barriers
Bibliographic record
Abstract
BACKGROUND: A significant gap exists between ideal evidence-based practice and real-world application of evidence-informed therapies for patients with hypoxaemic respiratory failure (HRF) and acute respiratory distress syndrome (ARDS). Pathways can improve the quality of care provided by helping integrate and organise the use of evidence informed practices, but barriers exist that can influence their adoption and successful implementation. We sought to identify barriers to the implementation of a best practice care pathway for HRF and ARDS and design an implementation science-based strategy targeting these barriers that is tailored to the critical care setting. METHODS: The intervention assessed was a previously described multidisciplinary, evidence-based, stakeholder-informed, integrated care pathway for HRF and ARDS. A survey questionnaire (12 open text questions) was administered to intensive care unit (ICU) clinicians (physicians, nurses, respiratory therapists) in 17 adult ICUs across Alberta. The Behaviour Change Wheel, capability, opportunity, motivation - behaviour components, and Theoretical Domains Framework (TDF) were used to perform qualitative analysis on open text responses to identify barriers to the use of the pathway. Behaviour change technique (BCT) taxonomy, and Affordability, Practicality, Effectiveness and cost-effectiveness, Acceptability, Side effects and safety and Equity (APEASE) criteria were used to design an implementation science-based strategy specific to the critical care context. RESULTS: Survey responses (692) resulted in 16 belief statements and 9 themes with 9 relevant TDF domains. Differences in responses between clinician professional group and hospital setting were common. Based on intervention functions linked to each belief statement and its relevant TDF domain, 26 candidate BCTs were identified and evaluated using APEASE criteria. 23 BCTs were selected and grouped to form 8 key components of a final strategy: Audit and feedback, education, training, clinical decision support, site champions, reminders, implementation support and empowerment. The final strategy was described using the template for intervention description and replication framework. CONCLUSIONS: Barriers to a best practice care pathway were identified and were amenable to the design of an implementation science-based mitigation strategy. Future work will evaluate the ability of this strategy to improve quality of care by assessing clinician behaviour change via better adherence to evidence-based care.
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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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".