Applying the Visual-Verbal Video Analysis Framework to Understand How Mental Illness is Represented in the TV Show Euphoria
Bibliographic record
Abstract
Mental illness in media can shape viewer’s beliefs about mental health, help-seeking, and empathic behaviors. The current study sought to investigate how mental health and substance use is depicted in popular media targeted for youth. The visual-verbal video analysis (VVVA) framework was applied to the HBO American drama television series Euphoria to understand how mental illness, substance use, and mental health service use is portrayed, and how characters respond to mental health scenes. Euphoria follows a group of high school students as they navigate adolescence, mental illness and substance use. The VVVA provides a framework for social science and medical researchers to qualitatively analyze multimodal information (e.g., text, cinematography, music and sounds, body language and facial expressions) of visual content. This commentary will briefly describe the VVVA framework, provide an overview of how the framework was applied and adapted to analyze a scene in the television series Euphoria, note similarities and differences to the original VVVA framework, and benefits and drawbacks. The VVVA framework was flexible and effective in coding various elements (e.g., body language, camera angles) in a scene in Euphoria.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".