Development and Validation of An Evaluation Scale for Audiovisual Production for Health Interventions - ZIKAMOB
Bibliographic record
Abstract
According to the World Health Organization, intervention actions and Health Education achieve better performance when based on Behavior Change Theories associated with new technologies. This work aimed to build and validate an Audiovisual Production Assessment Scale (APAS) for use in educational interventions. One hundred videos of up to 90 seconds in length, produced by high school students from Northeast Brazil, were analyzed. The APAS contains twenty statements, grouped into five sections, some of which are based on the Social Cognitive Theory (observational learning; facilitators) and others, such as the halo effect and cognitive comfort, were proposed by Daniel Kahneman. It was found that, of the twenty statements, 15 of them had no significant difference between different evaluators; having obtained a value of 0.941 for Cronbach's Alpha, showing excellent internal reliability of the APAS. On average, 22 (33.8%) videos received a score greater than 60 points, indicating that they have the potential to significantly contribute to population behavior change in relation to the prevention of mosquito-borne arboviruses; 28 (41.3%) contribute satisfactorily; 15 (22.9%), partially and from one to two videos were scored with values lower than 19 points. Altogether, 12% of the videos received maximum scores in relation to the total score and subjective score. The APAS is, therefore, an example of an effective tool for assessing audiovisual content that can be used in educational interventions in health, with good internal reliability. The scale allows evaluating any content, classifying the production into categories that reveal its potential to promote behavior change.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".