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Record W2794141788 · doi:10.1111/voxs.12413

Preparing your blood centre for accreditation

2018· article· en· W2794141788 on OpenAlexaff
Aline Wong, Y. K. Yuen, C. K. Lee

Bibliographic record

VenueISBT Science Series · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsCanadian Red Cross Society
Fundersnot available
KeywordsAccreditationDocumentationHealth careBusinessQuality assuranceQuality management systemAuditQuality (philosophy)MedicineQuality managementLegislationSpecialtyCertification and AccreditationOperations managementNursingMedical educationManagement systemFamily medicineAccountingEngineeringMarketingPolitical scienceService (business)Computer science

Abstract

fetched live from OpenAlex

Despite advance in medical treatment, blood supply is still an essential element in modern healthcare system with blood transfusion used in almost every clinical specialty. Adequacy, quality and safety are always the key focuses of concerns in most countries that measures have been taken to secure them. As in many other healthcare facilities, many countries have on its legislation to license the operation of blood centres (whether they are national, Red Cross or hospitals based) to provide the blood supply for clinical transfusion. The licensing requirement is based on their national health policy and law. Besides, like hospitals and clinical laboratories, many blood centres may also seek to achieve accreditation to demonstrate to their stakeholders of their commitment to ensure quality and safety. Such accreditation may vary from technical requirements related to medical laboratory testing as ISO 15189, professional related such as AABB to good manufacturing practice. Irrespective to the type and sources of accreditation, a superior quality management system is often necessary to provide the foundation to meet the accreditation's requirement with ongoing improvement. The study aimed to describe on the preparation of a blood centre towards accreditation. Few key elements will be outlined which include the top management commitment, selection of the right accreditation standard, training of the key personnel and organizational staff, gap analysis, documentation and change in practice if needed, internal and external audits. Tips and advice will also be included to ease the fear of accreditation processes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.143
GPT teacher head0.500
Teacher spread0.358 · 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.

Study designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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