The effects of culture and self-construal on responses to threatening health information
Bibliographic record
Abstract
OBJECTIVE: The current studies examined if cultural and self-construal differences in self-enhancement extended to defensive responses to health threats. DESIGN: Responses to fictitious medical diagnoses were compared between Asian-Americans and European-North Americans in experiment 1 and between Canadians primed with an interdependent versus an independent self-construal in experiment 3. In experiment 2, the responses of Chinese and Canadians who were either heavy or light soft drink consumers were assessed after reading an article linking soft drink consumption to insulin resistance. MAIN OUTCOME MEASURE: The primary-dependent measure reflected participants' defensiveness about threatening versus nonthreatening health information. RESULTS: In experiment 1, all participants responded more defensively to an unfavourable than a favourable diagnosis; however, Asian-Americans responded less defensively than did European-North Americans. In experiment 2, all high soft drink consumers were less convinced by the threatening information than were low soft drink consumers; however, among high consumers, Chinese changed their self-reported consumption levels less than did European-Canadians. In experiment 3, interdependence-primed participants responded less defensively to an unfavourable diagnosis than did independence-primed participants. CONCLUSION: Defensive reactions to threatening health information were found consistently; however, self-enhancement was more pronounced in individuals with Western cultural backgrounds or independent self-construals.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".