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Record W7098715329

J Can Acad Child Adolesc Psychiatry, 23:3, Fall 2014 167 FEATurE ArTIClE Portrayal of Youth Suicide in Canadian News

2014· article· en· W7098715329 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicGerman History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperContent analysisTheme (computing)Suicide preventionGuidelineHuman factors and ergonomicsQuality (philosophy)Public health
DOInot available

Abstract

fetched live from OpenAlex

Easson et al Objective: Responsible media reporting of youth suicide may reduce the risk of contagion and increase help-seeking behaviour. Accordingly, we conducted a content analysis of Canadian youth suicide newspaper articles to assess quality and summarize content (themes, age groups, populations and use of scientific evidence). Method: The Canadian Periodical Index Quarterly (CPI.Q) was searched (2008-2012) for full-text Canadian newspaper articles using the keywords “youth ” and “suicide. ” The top five most relevant articles as judged by CPI.Q were selected sequentially for each year (n=25). Quality was assessed using World Health Organization (WHO) guidelines for responsible media reporting. Content analysis was completed in duplicate by two reviewers. Results: All articles addressed youth suicide generally rather than reporting exclusively on a specific death by suicide. Alignment of articles with individual WHO guideline items ranged from 16 to 60%. The most common content theme was prevention (80%). No article was judged to glamorize suicide. Help seeking was addressed in 52 % of articles, but only 20 % provided information on where to obtain help. Statistics were referenced more frequently than scientific research (76 % vs. 28%). Conclusions: Our review suggests that Canadian media presents youth suicide as an issue for which hope and help exist. While the majority of reports aim to educate the public about suicide, increased use of scientific evidence about risk factors and prevention is recommended to facilitate the

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.196
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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