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Record W2302822978 · doi:10.1111/voxs.12203

Engaging ethnic minority blood donors

2016· article· en· W2302822978 on OpenAlexaffabout
Johanne Charbonneau, S. Daigneault

Bibliographic record

VenueISBT Science Series · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsHéma-QuébecInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsEthnic groupDiversity (politics)MedicinePublic relationsPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.009
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.260
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes2
Has abstractyes

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