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The Canadian donor health assessment questionnaire: can it be improved?

2006· article· en· W2066030722 on OpenAlexaffabout
Mindy Goldman, Shefali S. Ram, Qilong Yi, Sheila F. O’Brien

Bibliographic record

VenueTransfusion · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCanadian Blood ServicesUniversity of Ottawa
Fundersnot available
KeywordsInterviewRecallMedicineCognitive interviewPsychologyFamily medicineCognitionPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The donor health assessment questionnaire (DHAQ) plays an important role in ensuring blood safety. The Canadian DHAQ has been developed over many years on an ad hoc basis and has never been evaluated in light of cognitive science principles. In addition, donor attitudes about its mode of administration have not been assessed. STUDY DESIGN AND METHODS: Between May and August 2005, a total of 456 donors participated in the study by completing the DHAQ, followed by a short, scripted interview assessing recall (as an indicator of attention to the questions) of 17 specific items queried on the DHAQ and attitudes toward interviewer or self-administration of the DHAQ. RESULTS: Overall, 7.5 percent of donors were able to correctly identify all 17 items. Recall was best for questions asked as individual items (87%-99%) and decreased substantially for items that are part of a list (55%-91%). Position effects were demonstrated, with items at the end of a list being the most frequently forgotten. Twenty percent of repeat donors favored the current practice of interviewer administration of high-risk questions, whereas 80 percent were neutral or favored self-administration. CONCLUSION: The current format of the Canadian DHAQ is not optimal for donor attention to specific questions asked as part of a list. The majority of repeat donors are ready for a change in the method of administration of the DHAQ. Studies on donor recall may help guide evidence-based changes to the DHAQ.

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 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: none
Teacher disagreement score0.931
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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

Citations25
Published2006
Admission routes2
Has abstractyes

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