Point-of-care ultrasound implementation in internal medicine: Eliciting barriers and enablers using the theoretical domains framework
Bibliographic record
Abstract
Introduction: Evidence supports the use of point-of-care ultrasound (POCUS); however, POCUS is used in about 5% of internal medicine encounters. The theoretical domains framework (TDF) can be used to identify modifiable factors that influence health care practitioner behaviour. Method: TDF was used to identify barriers and enablers to POCUS adoption by internists in a large academic centre via a quantitative survey and qualitative semi-structured interviews. Results: Twenty-five (55%) internists completed the survey, and 10 were interviewed. Lack of time and training were identified as barriers by 100% (n = 25) and 80% (n = 20) of survey respondents, respectively. Lack of perceived benefit over current practices (4%, n = 1) and POCUS being outside an internist's scope of practice (8%, n = 2) were infrequently selected. In the interviews, nine domains were identified as relevant to changing POCUS behaviour. Four were barriers: (1) perceived inadequacy of training models and lack of repetition ( Skills, Reinforcements) that limit skill and comfort ( Beliefs about capabilities); (2) lack of time to perform and learn POCUS in a busy clinical environment ( Environmental context and resources); (3) diagnostic POCUS not being the standard of care or mandated by professional bodies ( Environmental context and resources) leading to a lack of relevance to practice ( Social/professional role and identity); and (4) perceived lack of added benefit to patient care or potential for patient harm ( Beliefs about consequences). Four key enablers identified were belief that procedural POCUS is a standard of care ( Environmental context and resources), perceived added benefit to patients ( Beliefs about consequences), colleagues’ enthusiasm about POCUS ( Optimism), and patient perceptions ( Social influences). Discussion: Using the TDF, barriers and enablers to POCUS uptake in a large academic internal medicine division were identified. Interventions to promote uptake should focus on promoting enablers and addressing barriers.
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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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".