Pretesting a Poster on Recommended Stress Management During the COVID-19 Pandemic in Indonesia: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 Peritraumatic Distress Index (CPDI) is a self-report questionnaire developed to evaluate the frequency of anxiety and depression symptoms among individuals during the COVID-19 pandemic. A recent study in China showed high CPDI scores among individuals in the 18-30 years age group and those over 60 years. During the COVID-19 outbreak, people were expected to maintain their mental health conditions, especially stress levels. Therefore, many national governments actively published health promotion media in an effort to educate the public. One such media developed by the Ministry of Health, Republic of Indonesia, was a poster titled "Hindari Stres dan Tetap Optimis dengan Melakukan Aktivitas Sehari-hari dan Tetap Menjaga Jarak." OBJECTIVE: The aim of this study is to conduct a test on a stress management recommendation poster developed by the Ministry of Health, Republic of Indonesia, in response to the COVID-19 outbreak by using pretesting communication theory. METHODS: In-depth interviews were conducted among 8 key informants and 1 graphic design expert. RESULTS: Pretesting can identify the strengths and weaknesses of media. The large amount of text and the lack of illustrations made the poster less attractive to readers. Moreover, there was a discrepancy between the title and contents of the poster. The poster was not able to persuade the informants to change their behavior in the near future. CONCLUSIONS: The poster was understood and accepted by the informants, but there was still much to be improved considering the poster was a product of the Ministry of Health, Republic of Indonesia.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".