Coping and Construal Level Matching Drives Health Message Effectiveness via Response Efficacy or Self-Efficacy Enhancement
Bibliographic record
Abstract
Five experiments examine the nature of different coping strategies and their subsequent effects on the effectiveness of health messages. We theorize that the two strategies of problem-focused versus emotion-focused coping are systematically associated with distinct construal levels (lower vs. higher), and thus messages cast at different levels of construal are differentially effective when a particular coping strategy is being activated. Specifically, we demonstrate that consumers primed with problem-focused strategies are more persuaded by messages presented at lower levels of construal, whereas consumers primed with emotion-focused strategies are more persuaded by messages presented at higher levels of construal. In addition, we posit that matching with each different type of coping strategy (problem-focused vs. emotion-focused coping) is driven by distinct types of efficacy processes. In particular, we demonstrate that the effects of a match with problem-focused coping are driven by self-efficacy, and the effects of a match with emotion-focused coping are driven by response efficacy. These findings make a significant contribution by building bridges between three theoretical traditions: coping, construal level, and efficacy in the context of health messaging.
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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 | 0.003 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".