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Record W3104240141 · doi:10.1016/j.lanwpc.2020.100047

A 24-hour online youth emotional support: Opportunities and challenges

2020· article· en· W3104240141 on OpenAlexaboutno aff
Paul S. F. Yip, Wai Leung Chan, Qijin Cheng, Shirley Chow, Siu Man Hsu, Yik Wa Law, Billie Lo, Ken Ngai, Kwai Yau Wong, Cynthia Xiong, Tsz Kong YEUNG

Bibliographic record

VenueThe Lancet Regional Health - Western Pacific · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersUniversity of Hong Kong
KeywordsPsychologyApplied psychology

Abstract

fetched live from OpenAlex

Suicide is a major global health problem with more than 800,000 deaths each year. It is the leading cause of death amongst the youth and they are always the ones who are difficult to engage [1,2]. Suicide and crisis hotlines are generally considered to be efficient and viable mechanisms by which to respond to the needs of suicidal individuals and those in mental health crisis, and direct them to suitable resources [3]. Young people have reported finding online and mobile interventions to be acceptable, low effort, and useful [4].

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0050.006
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0250.005

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 head0.514
GPT teacher head0.440
Teacher spread0.074 · 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 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

Citations36
Published2020
Admission routes1
Has abstractyes

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