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Record W3010319928 · doi:10.1177/1524839920910695

Increasing Awareness of a Provincial Mental Health Resource for Boys and Young Men 12 to 17 Years: Reflections From Foundry’s Province-Wide Campaign

2020· article· en· W3010319928 on OpenAlexaffabout
Marco Zenone, Paul W. Irving, Michelle Cianfrone, Leah Lockhart, Stefanie Costales, Kathryn Cruz, Jamie Ignacio

Bibliographic record

VenueHealth Promotion Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsProvidence Health CareBC Children's Hospital
Fundersnot available
KeywordsSocial mediaMental healthAgency (philosophy)Promotion (chess)AnalyticsLeverage (statistics)Health promotionPsychologyPublic healthSociologyPolitical scienceMedicinePoliticsComputer scienceNursingSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

Foundry empowers youth and young adults aged 12 to 24 years to lead healthy lives through a province-wide network of centers and online resources in British Columbia, Canada. However, initial evaluation data gathered from Foundry centers have shown that boys and young men are half as likely to access Foundry compared to girls and young women. To address this need, we set out to understand why boys and young men aged 12 to 17 years aren't accessing mental health supports and to develop a promotional campaign to connect them with the resources available through Foundry. A campaign concept called "Everything Is Fine" was chosen; the campaign depicts boys and young men trying to appear as if they are OK, even though their facial expressions clearly show they are holding back stress. The campaign concept was chosen through an iterative process of research and testing. Promotion materials were created for social media (Instagram, Snapchat) and school posters, which were distributed across British Columbia, Canada . Evaluation was conducted through social media analytics and google analytics. Pre- and postsurveys were also distributed to two school districts to assess recognition of Foundry. Approximately 160,000+ persons viewed the media on Instagram, while 170,000+ viewed on Snapchat. There was a 70% increase in website traffic compared with the 3 months prior (18,881 vs. 11,126). In the surveyed school districts, Foundry awareness increased by 10% and 15%. The lessons learned from our campaign were to prioritize research and to leverage media agency experience for large 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.603

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0230.004
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0060.000

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 head0.092
GPT teacher head0.403
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations11
Published2020
Admission routes2
Has abstractyes

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