96 Introducing an Online Specialist in Blood Bank (SBB) Program to the Global Laboratory Community
Bibliographic record
Abstract
The main challenge to providing distance education to international students is a political one. The Department of Homeland Security prohibits issuing student visas for distance students to enter the United States, but students who attend courses on-site can acquire student visas. As a result of this policy, the international students cannot come to the US for clinical practicums. The Rush University Specialist in Blood Bank (SBB) Certificate Program was developed in 2007 to meet the needs of experienced medical laboratory scientists seeking advanced knowledge of immunohematology and its related disciplines. In 2010, the Master of Science in Clinical Laboratory Management (CLM) was introduced to address the demand for skills in management. Initially, it began as a program for laboratory professionals in the United States, but recently has grown into a global program including students from Canada, Singapore, Jamaica, and Trinidad and Tobago. The program utilizes an online methodology for lectures, discussions, and assessments, which enables students to participate worldwide. Clinical experiences are located at blood centers and hospitals near the student’s home, and utilize a comprehensive checklist and verification by the pathologist in charge. The second-year curriculum in CLM is an option for all students completing their SBB certificate program. Since the inception of the SBB program, 120 SBB certificates have been awarded. Forty-seven students have graduated with a Master of Science in CLM (40 from the SBB/CLM program and seven from the CLM-only program). The success of the Rush University programs is built on the quality and accessibility of online education to reach students regardless of their geographic location. Through this online SBB/CLM program, global transfusion safety is being promoted and emphasized.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.033 |
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".