Red blood cell inventory management: Insights from transfusion laboratory technologists in <scp>British Columbia</scp>, <scp>Canada</scp>
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is concern about sustaining the O negative blood supply, especially in areas with many rural/remote hospitals like British Columbia. Red blood cells are perishable, making inventory management challenging. Demand must be met without wasting this precious resource. Inventory management challenges stem from data scarcity and human factors. Transfusion medicine technologists, who manage inventory daily, are key to understanding the human factors in inventory management. We conducted a qualitative study to understand technologists' inventory management perspectives and experiences, particularly for group O negative red blood cells, aiming to inform future inventory modelling to address human factors. MATERIALS AND METHODS: We interviewed transfusion laboratory technologists and technical leads from all health authorities and a blood product supplier representative for the Province of British Columbia. Thematic analysis of the interview transcripts was conducted. RESULTS: We found five themes that influence technologist decision-making on RBC inventory management, key challenges for O-negative RBCs, and identified inventory management strategies. We compare the top three inventory practices from our results with literature. CONCLUSIONS: Our findings help bridge the knowledge gap concerning human factors in RBC inventory management, with potential generalizability to other jurisdictions. They hold promise for informing the safeguarding of donors' altruistic contributions.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".