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Record W2771392839

Make it personal: Commentary on the paper, 'Evaluation of a campaign to improve awareness and attitudes of young people towards mental health issues' (Livingston et al., 2012)

2013· article· en· W2771392839 on OpenAlexaboutno aff
Debra Rickwood

Bibliographic record

VenueEducation and health · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsStigma (botany)Mental healthMental health literacySocial mediaPsychologyMedia literacyMental illnessSocial stigmaPublic relationsSocial psychologyPsychiatryMedicinePolitical sciencePedagogyFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

The article by Livingston et al. (2012) reporting the evaluation of a campaign to improve awareness and attitudes of young people towards mental health issues makes some insightful points regarding the impact of media campaigns on the stigma of mental illness. The particular Canadian campaign evaluated featured a prominent male sports figure talking about mental health issues and used online social media to reach young people. The effectiveness of the campaign was shown to be limited to the proximal outcomes of increasing awareness and use of a mental health website, but did not impact the distal outcomes to improve attitudes toward people with mental illness. The authors acknowledge that these findings are consistent with Corrigan’s recent summary that such campaigns show evidence of penetration but little meaningful impact (Corrigan, 2012). Notably, this campaign used social media rather than traditional media, which would be expected to have a greater impact for young people, but the anticipated outcomes remained elusive. Livingston et al. make the points, however, that increased market penetration is a worthwhile outcome and that improving mental health literacy, rather than reducing stigma, should be the goal of such media campaigns.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.053
GPT teacher head0.410
Teacher spread0.357 · 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 designObservational
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

Citations1
Published2013
Admission routes1
Has abstractyes

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