Examining Patient Safety Events Using the Behaviour Change Wheel: A Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Precursor-level safety events (PSEs) pose greater patient risk than no-harm events but are not as severe as serious safety events. Despite their potential for harm, the underlying determinants associated with PSEs are poorly understood. This study aimed to use a behavior change framework to understand the underlying determinants of PSEs and whether associated action items aligned with the behavior. METHODS: This cross-sectional study took place in a maternal/pediatric hospital. A total of 58 prerecorded PSEs were analyzed using the Behaviour Change Wheel (BCW); a behavioral framework that identifies sources of behavior and proposes intervention types that address said behavior. Researchers and clinicians independently coded each PSE's underlying determinant and action items using the relevant components of the BCW. The types and frequency of underlying behavioral determinants and intervention types for each PSE were documented. A matrix, based on the BCW, reflected how often the underlying behavior aligned with the corresponding action item. RESULTS: Of the 58 PSEs, six behavioral determinants and seven intervention types were identified. Environmental context/resources was the behavioral determinant coded most often (25.4%); education was the most common intervention type (45.8%). Several underlying determinants (24.6%) and action items (8.3%) received no code due to limited information. Based on the BCW matrix, 34.2% of behavioral determinants were addressed with interventions that would target the underlying behavior, while 37.8% did not align, and 28.1% could not be coded due to missing behavioral information. CONCLUSION: This study identified poor alignment between types of interventions and underlying determinants in more than one third of analyzed PSEs. This included using educational interventions in about 50% of events, despite this type of intervention being ineffective for most of the coded behaviors. Further, alignment of many safety events could not be determined due to limited reported information. This highlights a need to design more systematic, behavior-informed approaches to reporting PSEs and identifying interventions to effectively change behavior.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".