Spanish media coverage of youth mental health issues during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The media portrayal of mental health is relevant in shaping the population's attitudes towards mental disorders. However, there is little information about the representation of young mental health issues in the Spanish-language press, especially in the context of the COVID-19 pandemic. The general objective of this study was to analyse the tone and content of Spanish newspaper articles about mental disorders in youth during the COVID-19 pandemic. METHODS: We collected media articles from the 10 most read news sources over a 6 month period (January-June 2021). These articles were coded for content using a standardised codebook, followed by an inductive thematic analysis. A total of 205 news items were evaluated. RESULTS: Results showed that the majority of the news items had an overall positive tone (68.3%), only 5.4% were stigmatising and only 7.3% were related to violence. However, few articles offered help seeking information (6%), adolescents were rarely quoted (14%) and children were never quoted. Substantial differences are described in terms of age, gender and disorder. The thematic analysis led to three emergent themes: (i) violence and victimisation; (ii) the COVID-19 pandemic; and (iii) technology and social media. CONCLUSIONS: The percentage of news in the Spanish media that refer to young people's mental health in a stigmatising way or in a way associated with violence is very low. Furthermore, the COVID-19 pandemic may have promoted more positive discussion about youth mental health. However, major challenges remain to be addressed, as patients are seldom quoted, very few articles offer help-seeking information, and a narrative of victimisation without appropriate discussion of resilience regularly occurs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".