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Record W1987399361 · doi:10.1007/s12160-011-9315-8

Applied Tension and Coping with Blood Donation: A Randomized Trial

2011· article· en· W1987399361 on OpenAlexafffund
Crystal Holly, Lindsey Torbit, Blaine Ditto

Bibliographic record

VenueAnnals of Behavioral Medicine · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsDonationAnxietyBlood donorBlood pressureMedicineVasovagal syncopeCoping (psychology)Psychological interventionRandomized controlled trialIntensive care medicinePhysical therapyAnesthesiaSurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the ongoing need for blood donation, few people give blood. A common reason is concern about vasovagal symptoms. PURPOSE: The aim of this study was to evaluate the effectiveness of applied tension in reducing vasovagal symptoms during blood donation and the mechanisms of action. METHOD: Two hundred eighty-two young adult blood donors were randomly assigned to conditions involving applied tension during the pre-donation wait period, during the blood draw, both, or no applied tension at all. RESULTS: Applied tension was effective in reducing vasovagal symptoms in blood donors, particularly when practiced during the pre-donation wait period (p < 0.001). People who practiced applied tension during the pre-donation wait period required less treatment for vasovagal reactions than people who did not (8% vs. 16%). CONCLUSIONS: The results of this study suggest that the effects of applied tension on vasovagal symptoms are not mediated entirely by exercise-related changes in blood pressure and heart rate. Rather, it may reduce anxiety or physiological consequences of anxiety. Applied tension is a useful treatment which can help people cope during blood donation and other invasive medical interventions.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.586

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.118
GPT teacher head0.320
Teacher spread0.202 · 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 designRandomized trial
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
Published2011
Admission routes2
Has abstractyes

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