The Role of Online Media in Promoting Pet Well-Being During the COVID-19 Pandemic: A Sustainability Perspective
Bibliographic record
Abstract
In health promotion efforts, online media have functioned as educational tools by forming pet well-being coverage in the news.During the current COVID-19 pandemic, education through information on how to interact between pet owners and their pets is very important.The purpose of this study is to describe the role of online media in disseminating information about pet well-being in a pandemic situation.The research was conducted qualitatively on five Indonesian online media, with the selection of the analysis unit for the news at tribunnews.com, detik.com,kompas.com,okezone.com,and cnnindonesia.com.The basis for selection is based on the order of the five best online news portals in Indonesia in 2020.The news selected according to the criteria is a feature type that contains how humans and pets interact during the pandemic.The analysis technique used mixed methods in media text analysis, with quantitative content analysis for mapping news emergence and qualitative framing analysis for elaborating media frames.The findings of this study show that online media have roles in health promotion supported by coverage emphasizing mitigation with a focus on ways to prevent transmission of COVID-19 in pets and the interactions that owners need to make with their pets.The conclusion of this study is that online media has a role as a supporting media in health promotion, as well as being used as a means of educational media.However, the dissemination of health information on online media still needs to be presented more intensively, integrated with other issues, and educational in nature, not only reported in lifestyle and health rubrics.The contribution of this study is regarding the role of online media in providing education to support the sustainability of urban health by shaping health literacy regarding the importance of balancing interactions between humans and their pets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".