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Record W2509623544 · doi:10.2450/2016.0010-16

The relative efficacy of three interventions to favour return to give blood.

2017· article· en· W2509623544 on OpenAlexaff
Geneviève Myhal, Gaston Godin, Sophie Dubuc

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsUniversité LavalHéma-Québec
Fundersnot available
KeywordsPsychological interventionBlood donorDonationMedicineBlood donationsReport cardIntervention (counseling)Family medicinePsychologyNursingImmunologyLaw

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to test the relative efficacy of action planning and reward distribution to promote retention of whole blood donors. MATERIALS AND METHODS: A sample of 7,399 donors was randomised to one of three interventions: "action planning" (n=2,585); "reward" (n=2,397); and "thank you" (n=2,417). Participants in the action planning condition were invited to write the date of their next donation on a post-it note before taking it home at the end of the donation process. Participants in the "reward" condition were given an anti-theft credit card sleeve at the end of the donation process. The "thank you" intervention is the usual condition at the end of the process; it was considered the control condition. The dependent variable was the proportion of donors who registered to give blood at six months. RESULTS: Overall, 4,444 (60.06%) donors registered to give blood at six months. There were no differences between the three interventions in the proportions of donors who registered to give blood (d.f. 2, chi-square=3.72, p<0.15). However, gender modified the effect of the intervention (d.f. 2, chi-square=6.57, p<0.0375); more women registered in the "thank you" condition than in the other two. DISCUSSION: The results suggest that action planning and the distribution of a reward have no motivational effect on the return to give blood. Nonetheless, women appear to respond more negatively to these interventions at the end of the donation process.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.061
GPT teacher head0.282
Teacher spread0.221 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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