Improvement in Hematology Interprofessional Care: Simulation With an Emphasis on Collaboration
Bibliographic record
Abstract
Introduction: For many training programs, including hematology, there are limited structured opportunities to practice collaboration as a competency. Training is often limited to ad hoc interactions during clinical rotations. Accordingly, there is further need for immersive and standardized collaboration educational programs. This pilot study explored simulation for developing and assessing collaboration competency among hematology residents. Methods: Two standardized simulation center scenarios were developed that required residents to work in interprofessional teams. The objectives were to develop collaboration competence and confidence through experiential learning and facilitated reflection. Team members included education and simulation experts as well as hematology nurses as embedded participants. Case 1 presented a 72-year-old male with stage 4 lymphoma experiencing shortness of breath during a rituximab infusion. Case 2 presented a 68-year-old male who suffered a provoked pulmonary embolism. Both cases utilized a simulated clinic space. Pre, post, and 3-month questionnaires (self-assessed collaboration competency and simulation evaluation) were completed. Each session included structured debriefing with facilitated reflection focused on collaboration. Results: Seven senior hematology subspecialty residents participated. Despite residents entering the simulation cases with confidence in collaboration, higher collaboration confidence ratings were observed on postsimulation questionnaires (8.2 vs. 7.6 on a 10-point Likert scale). Residents demonstrated awareness of appropriate collaboration skills, but at times failed to implement knowledge into action. Facilitated reflection during the debrief helped residents critique their collaboration performance and develop improvement plans. Discussion: Simulation is a promising tool for teaching and assessing collaboration within hematology training.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".