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Perceptions and motivations of Canadian autologous blood donors

2001· article· en· W2114814792 on OpenAlexaffabout
Catherine Moltzan, Normand L. Proulx, Janis Bormanis, Nedra R. Lander, H DeGroot, G. Rock

Bibliographic record

VenueTransfusion Medicine · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsDonationMedicineBlood donorSAFERDemographicsPerceptionAutologous bloodFamily medicineHealth careSurgeryIntensive care medicinePsychologyImmunology

Abstract

fetched live from OpenAlex

Preoperative autologous blood donation in Canada has increased in the last decade due to concerns about allogeneic blood safety. As economic policies necessitate the validation of autologous blood donation, it is important to assess potential changes. This study examines the motivations and perceptions of patients participating in a Canadian autologous blood donation programme. The study was conducted at the Ottawa Hospital, Civic Campus. Questionnaires were developed and then administered to 100 consecutive adult autologous blood donors. The questionnaires assessed patient demographics, motivation for participation and perceptions about blood safety. Autologous donation was presented to 82% of patients as an option by their physicians. However, 59% of all patients felt that they were motivated to donate by their own fears. Patients (87%) also felt that directed blood donation should be offered due to their perception that directed donation is safer. Three-quarters of patients would be willing to pay for autologous donation. Patients are concerned about the safety of allogeneic blood and they are motivated to seek other alternatives despite the fact that allogeneic blood is very safe. These perceptions and motivations must be taken into account as health care policy changes are considered.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.241
Teacher spread0.219 · 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 designObservational
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

Citations18
Published2001
Admission routes2
Has abstractyes

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