Some feelings are more important: Cognitive attitudes, affective attitudes, anticipated affect, and blood donation.
Bibliographic record
Abstract
OBJECTIVE: The present research assessed the simultaneous effects of four attitude variables (cognitive attitudes, affective attitudes, anticipated negative affective reactions, and anticipated positive affective reactions) in the context of the theory of planned behavior (TPB) on blood-donation intentions and behavior. METHODS: Experienced blood donors (N = 1108) completed questionnaires measuring attitude variables plus components of the TPB and a measure of attitudinal ambivalence in relation to giving blood again in the next six months. Records were used to assess whether participants subsequently donated blood again in the six months after completing the questionnaire. The main outcome measures were objectively assessed blood donation and intentions to make an additional donation of blood. RESULTS: Confirmatory factor analysis supported a distinction between cognitive attitudes about giving blood, affective attitudes about giving blood, anticipated negative affective reactions about not giving blood, and anticipated positive affective reactions about giving blood. Multiple regression analyses indicated that perceived behavioral control, anticipated negative affective reactions, cognitive attitude, anticipated positive affective reactions and subjective norms were significant simultaneous predictors of intentions to donate blood. Logistic regression analyses indicated that intentions, perceived behavioral control, and anticipated positive affective reactions were significant, simultaneous predictors of blood donation. Attitudinal ambivalence significantly moderated the effects of cognitive attitudes on intentions, and the effects of anticipated negative affective reactions on both intentions and donation behavior. CONCLUSION: The findings point to the value of considering different types of attitudes, and anticipated negative affective reaction in particular, for predicting health behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".