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Record W3127741572 · doi:10.3389/fpsyt.2021.627588

Mediating Effect of Hope on the Relationship Between Depression and Recovery in Persons With Schizophrenia

2021· article· en· W3127741572 on OpenAlexaboutno aff
Sri Padma Sari, Murti Agustin, Diyan Yuli Wijayanti, Widodo Sarjana, Umi Afrikhah, Kwisoon Choe

Bibliographic record

VenueFrontiers in Psychiatry · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
FundersChung-Ang University
KeywordsSchizophrenia (object-oriented programming)PsychologyDepression (economics)Mental healthPsychological interventionClinical psychologyPsychiatryScale (ratio)

Abstract

fetched live from OpenAlex

Background: Depression and hope are considered pivotal variables in the recovery process of people with schizophrenia. Aim: This study examined the moderating effect of depression on the relationship between hope and recovery, and the mediating effect of hope on the relationship between depression and recovery in persons with schizophrenia. Methods: The model was tested empirically using the data of 115 persons with schizophrenia from Central Java Province, Indonesia. The Calgary Depression Scale for Schizophrenia, Schizophrenia Hope Scale-9, and Recovery Assessment Scale were used to measure participants' depression, hope, and recovery, respectively. Results: The findings supported the hypothesis that depression moderates the relationship between hope and recovery, and hope mediates the relationship between depression and recovery. Conclusions: The findings suggest that mental health professionals need to focus on instilling hope and reducing depression to help improve the recovery of persons with schizophrenia. Furthermore, mental health professionals should actively develop and implement programs to instill hope and continuously evaluate the effectiveness of the interventions, particularly in community-based and in-patient mental health settings.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.324

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 integrity0.0000.001
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 head0.063
GPT teacher head0.357
Teacher spread0.294 · 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

Citations34
Published2021
Admission routes1
Has abstractyes

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