Developing Multimedia Social Impact Entertainment Programming on Healthy Ageing for Hispanics in the United States
Bibliographic record
Abstract
Social impact entertainment programming has roots in the development communication strategy known as entertainment-education. Social impact entertainment programming has gained traction in recent years in the United States, with media studios and corporations now operating social impact divisions and academic centres studying the effects of mass media projects designed to inspire change. There is a gap in the literature, however, of formative research conducted to describe the development and design of these programs. This gap contributes to a lack of information not only to replicate this work, but also a weakness in understanding underlying theoretical mechanisms required to foster positive change across emerging social and behaviour change communication strategies. This article analyses qualitative interviews to understand the creation of a social impact entertainment program on healthy ageing for Hispanics on Univision, the U.S.-based Spanish-language network. Healthy ageing cuts across the sustainable development goals and is a health priority for diverse, global populations as more people are living longer. The study concludes that media professionals were compelled by higher-value inclinations; the process of creating tailored healthy ageing content included strategy and research integration; and materials included television, digital, social media, and community events. Implications for development communication practitioners and scholars are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".