Framing health messages in short-form videos: a moderated mediation model with medication belief and susceptibility to informational influence
Bibliographic record
Abstract
Purpose This study explores how message framing in short-form health videos influences medication purchase intention and the underlying mechanisms. Design/methodology/approach Three experiments were conducted. Study 1 investigated the relationships between message framing, negative emotions, and medication purchase intention. Study 2 tested the moderating role of medication belief in a moderated mediation model using a 2×2 between-subjects design, and Study 3 examined that of susceptibility to informational influence using a one-factor between-subjects design. Findings Consumers showed higher over-the-counter medication purchase intention after watching loss-framed messages in treatment-oriented short-form health videos. These messages evoked negative emotions, which in turn increased medication purchase intention. Medication belief and susceptibility to informational influence moderated the mediating effect of negative emotions, such that the effect was enhanced among individuals with high medication belief or high susceptibility to informational influence. Practical implications Pharmaceutical marketers should strategically deliver loss-framed messages in short-form health videos to effectively promote over-the-counter medications. Originality/value The study extends the framing effect to treatment-oriented short-form health videos. It reveals the mediating role of negative emotions and the moderating effect of medication belief and susceptibility to informational influence within a moderated mediation framework, highlighting key emotional and individual-level factors in digital health decisions.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".