Barriers and enablers to implementing intranasal ketamine for Primary Care Paramedics in Canada – A parallel convergent mixed methods study
Bibliographic record
Abstract
Introduction British Columbia Emergency Health Service trialled the use of intranasal (IN) ketamine given by Primary Care Paramedics (PCPs). Prior to this practice change, the PCPs had not performed weight-based drug calculations, given medications intranasally, nor been responsible for controlled and targeted substances. This study aimed to use the Capability, Opportunity, Motivation and Behaviour (COM-B) model and Theoretical Domain Framework (TDF) to identify enablers and barriers to implementing IN paramedic administered ketamine analgesia (iPAKA) for PCPs. Methods This was a parallel convergent mixed methods study with two phases. The quantitative phase consisted of longitudinal staff surveys to assess PCP knowledge and perceptions of ketamine and controlled and targeted substances policies. The qualitative phase involved staff focus groups on programme implementation. Descriptive statistics of survey results were integrated with coded focus group data and analysed using the COM-B model and TDF. Evidence-based behavioural change techniques were mapped to each TDF domain. Findings Our analysis revealed barriers and enablers across several TDF domains. Implementing ketamine was enabled by quality education, strong organisational support and the availability of cognitive aides. Trial success was attributed in part to participant's feelings of optimism and their increased job satisfaction. Key barriers included a knowledge gap involving drug dosage calculations, negative emotions associated with performance anxiety and a lack of field education and supervision to monitor paramedic practice. Conclusion The use of theoretical frameworks and models like COM-B/TDF serves to improve the sustainable implementation of behaviour and clinical practice change in paramedicine. When project teams use theory to guide design and implementation, they can systematically identify and target individual and organisational enablers and barriers to adopting routine practices. The iPAKA study reveals key barriers and facilitators in several TDF domains and presents theory-linked targeted behavioural techniques to support on-going implementation of PCP-administered IN ketamine for analgesia.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".