Bibliographic record
Abstract
Background and Objectives Targeting ethnic minorities is consistent with the objective of ensuring better access to rare phenotypes. The number of blood donors is generally lower among these groups. This article seeks to present the solutions Héma‐Québec has developed to address this challenge. Materials and Methods In 2010–2011, the organization conducted 53 awareness‐raising activities targeting black communities. In 2009–2010, 83 interviews were conducted with donors and leaders of diverse ethnic groups in Montreal. Employees’ perceptions were also explored. Based on research findings, a training seminar was developed and provided to 69 front‐line managers. Its most important elements were integrated into basic training for all employees. Results The number of black community donors climbed from 170 in 2009 to 1582 in 2012. However, experiences with ethnic associations and donors have raised many concerns among staff. The 2‐day training helped planning services develop better recruitment strategies and bolster employees’ self‐confidence with regard to their interactions with ethnic minorities. Conclusion New strategies are dependent on the specific characteristics of each country's ethnic diversity, the availability of empirical data on minorities, and the clearly expressed will of management in blood products supply organizations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".