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Record W2132386540 · doi:10.5539/gjhs.v4n1p33

Effect of an Education Program on Improving Help-Seeking among Parents of Junior and Senior High School Students in Japan

2011· article· en· W2132386540 on OpenAlexvenueno aff
Hatsumi Yoshii, Yuichiro Watanabe, Hideaki Kitamura, Nan Zhang, Kouhei Akazawa

Bibliographic record

VenueGlobal Journal of Health Science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsIntervention (counseling)Promotion (chess)MedicineSchizophrenia (object-oriented programming)Quality of life (healthcare)Family medicinePsychiatryPsychologyNursing

Abstract

fetched live from OpenAlex

Early intervention in schizophrenia is important for patient prognosis and quality of life. At the time of the first episode, quality of life is influenced by identification of symptoms and by medical help-seeking behavior. In this prospective cohort study, we investigated help-seeking among 2690 parents of junior and senior high school students before and after the parents viewed a newly developed web-based education program aimed at improving knowledge of schizophrenia. Our web-based education program aimed to improve understanding of schizophrenia, including promotion of help-seeking. Many parents (33.1%-50.0%) consulted a physician in a department of psychosomatic medicine when their child experienced symptoms. Characteristics that predicted a decision not to seek psychiatric medical help were having child with all symptoms, younger parent age, and lower family income (p<0.05). After the education program, the rate of parents who sought medical help within 1 week was significantly higher for all symptom categories except sleeplessness (p=0.001). These findings suggest that the present web-based education program was useful in promoting medical help-seeking behavior among parents of junior and senior high school students in Japan.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.372
Teacher spread0.358 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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