Determining staffing requirements for blood donor clinics: the <scp>C</scp>anadian <scp>B</scp>lood <scp>S</scp>ervices experience
Bibliographic record
Abstract
BACKGROUND: Canadian Blood Services runs approximately 16,000 donor clinics annually. While there were more than 220 different clinic configurations used in 2011 and 2012, 67% of all clinic configurations followed one of 51 standard models. As part of operational planning for current and future configurations it was necessary for Canadian Blood Services to calculate staffing requirements for standard clinic models. STUDY DESIGN AND METHODS: In this article we present a method that incorporates both cost control and impact on donor experience. We calculate staffing requirements to minimize costs, but adjust using queuing theory to ensure donor wait time metrics are met. The method can be applied in a wide variety of situations. RESULTS: Although developed for a particular study, the methods described in this article can be applied in a wide variety of situations. A case study in which the model is used to review existing staffing arrangements at Canadian Blood Services is presented. CONCLUSION: The staffing model can be used to balance the requirements of minimizing staffing costs with that of ensuring that donors do not suffer unnecessary delays. Moreover, in an example application, savings of 3.4% were identified through the modeling process.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".