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Record W2191568915 · doi:10.5539/ass.v11n27p281

The Status Quo Survey and Countermeasure Analysis of Chinese Netizens’ Needs for E-Mental Health Services

2015· article· en· W2191568915 on OpenAlexvenueno aff
Jian Zhao

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesSouthwest University
KeywordsMental healthStatus quoPromotion (chess)Service (business)Public relationsMental illnessPsychologyMental health serviceCountermeasureBusinessPolitical sciencePsychiatryMarketingLaw

Abstract

fetched live from OpenAlex

The e-Mental Health Service refers to the work for mental health promotion provided, following the laws of mental health, by professional institutions and professionals to netizens through the Internet. A survey of 1588 netizens shows that netizens have a need for e-Mental Health Services and higher needs for mental health knowledge; they show obvious social orientation in their choices of service providers; demographic variables have remarkable influences on netizens’ specific needs; and netizen groups, there are relatively higher needs in female, college degree holders and above, the youth and brain workers, such as students, teachers, company employees, and staff in public institutions. The results of the survey indicate that netizens’ needs for e-Mental Health Services are complex and diversified, and netizens’ understanding and demand for professional service institutions, professional service providers and electronic service modes still need to be improved.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.464
Teacher spread0.393 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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