A situational test of the health belief model: How perceived susceptibility mediates the effects of the environment on behavioral intentions
Bibliographic record
Abstract
OBJECTIVE: Existing evidence regarding the role of perceived susceptibility in shaping preventative health behavior is mixed for the Health Belief Model (HBM). To clarify whether and under which conditions perceived susceptibility affects preventative behavior, this study aims to better understand how situational environmental factors affect perceived susceptibility, thereby shaping health decisions, and whether this mediation relationship is conditioned by other HBM cognitions, namely perceived benefits and severity. METHODS: Therefore, we employed a scenario-based experiment in a large, representative sample of the German population (N = 4,802) in April 2022. Respondents were presented with a fictional invitation to a social gathering, which mimicked a post in a messenger group chat. The invitation included five experimentally manipulated scenarios: no COVID-19 preventative measure implemented, a COVID-19 test is required; either testing negative, being vaccinated, or being recovered from COVID-19 is required (known as 3G in the German context); reduced number of attendees; or the social gathering occurred outside. Moreover, perceived susceptibility to contract COVID-19 at the social gathering and perceived severity and benefits (independent of the scenario) were measured. RESULTS: We found evidence that perceived susceptibility mediates the relationship between each implemented preventative measure and willingness to attend the social gathering. The effect of the preventative measures on perceived susceptibility and the indirect effect of the preventative measure on attendance via perceived susceptibility were moderated by perceived benefits. However, there is lack of robust evidence that perceived severity moderates the effect of perceived susceptibility on attendance. CONCLUSION: In summary, our study provides evidence that individuals perceive and adapt their perceptions and behavior to preventive measures in a given situation, which speaks to the dynamic nature of the cognition perceived susceptibility. Moreover, our findings suggest a promising avenue forward for the HBM is to examine how the cognitions and the environment together shape preventative health behavior.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".