Barriers and potential solutions toward optimal prophylaxis against deep vein thrombosis for hospitalized medical patients: A survey of healthcare professionals
Bibliographic record
Abstract
OBJECTIVE: Deep vein thrombosis (DVT) prophylaxis remains underused in hospitalized medical patients despite strong recommendations that at-risk patients should receive prophylaxis. To understand this gap between knowledge and practice, we surveyed clinicians' perceptions about the importance of DVT prophylaxis, barriers to guideline implementation, and interventions to optimize prophylaxis. METHODS: Paper- and electronic mail-based surveys were sent to 1553 internists, nurses, pharmacists, and physiotherapists in Ontario, Canada. Responses were scored on 7-point Likert scales. An important barrier to optimal DVT prophylaxis was 1 with a mean score ≥5, and interventions with high potential success or feasibility were those with mean scores ≥5. RESULTS: DVT prophylaxis was perceived as important by all clinician groups but this did not appear to translate into knowledge about underutilization of current DVT prophylaxis strategies. Physicians and pharmacists recognized the underuse of DVT prophylaxis in medical patients, while nurses and physiotherapists tended to perceive prophylaxis strategies as appropriate. Lack of clear indications and contraindications for prophylaxis and concerns about bleeding risks were perceived as important barriers. Preprinted orders were considered the most potentially successful and feasible way to optimize prophylaxis. CONCLUSIONS: A considerable barrier to optimal DVT prophylaxis utilization may be that those healthcare providers best able to conduct a daily assessment of patients' need for prophylaxis underrecognize the problem that prophylaxis is underutilized in this population. Interventions to bridge the gap between knowledge and practice should consider preprinted orders outlining DVT risk factors, and educating front-line care providers prior to implementation of a top-down approach.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".