Helping me, helping you: self‐referencing and gender roles in donor advertising
Bibliographic record
Abstract
BACKGROUND: Donor advertising typically emphasizes altruism, but an appeal to individual self-interest may be more effective in heightening blood donation intentions among youthful nondonors. STUDY DESIGN AND METHODS: A total of 292 undergraduate business students at a Canadian university provided complete data in response to a between-subjects full-factorial advertising experiment with sex, self-referencing, and message strategy factors. Self-referencing, or mental processing that links information to the self-concept, was elicited at either a low or moderate level, whereas the message strategy was either agentic (donate blood because you may need it yourself) or communal (donate blood because someone close to you may need it). Dependent variables included identification with the ad, donation intentions, and a discrimination measure of recognition memory. RESULTS: A three-way interaction among sex, self-referencing level (low or moderate), and message (agentic or communal) was found. Two-way self-referencing by message graphs of donation intentions and ad identification showed a parallel structure for males in that their responses were generally more favorable when self-referencing was at a moderate level, regardless of the message type. Among women, however, crossover interactions between the level of self-referencing and the message type (agentic vs. communal) were observed, such that the message's effect differed with the level of self-referencing. For both men and women, the agentic message was more effective than communal ad copy when a moderate level of self-referencing was achieved. CONCLUSION: Collection agencies should consider appealing to young nondonors by suggesting that they give blood to make it available for themselves if required.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".