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Record W1985319425 · doi:10.1159/000228839

Profiles of Suicidality and Clusters of Hungarian Adolescent Outpatients Suffering from Suicidal Behaviour

2009· article· en· W1985319425 on OpenAlexaff
Elek Dinya, János Csorba, Zsuzsa Sörfőző, Peter M. Steiner, Beáta Ficsor, Ágnes Horváth

Bibliographic record

VenuePsychopathology · 2009
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsDysfunctional familyPsychologyClinical psychologyPsychiatryCoping (psychology)Cluster (spacecraft)Depression (economics)Poison controlSuicide preventionMajor depressive disorderMedicineCognitionMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to reveal the background dimensions of suicidal behaviour (SB) and to identify clusters of Hungarian adolescent outpatients suffering from suicidality by means of the following correlates of SB: depression, inadequate conflict-solving methods, dysfunctional attitudes, maladaptive coping, help-seeking strategies and negative life events. SAMPLING AND METHODS: A self-report test battery was completed by every consecutive new adolescent outpatient from a representative patient pool of 5 local child psychiatric centres in Western Hungary over an 18-month period (n = 644). The questionnaires used were the pilot version of the Columbia Depression Scale, the Hungarian standard versions of the Beck Depression Inventory, the Ways of Coping Questionnaire, the Dysfunctional Attitude Scale and the Junior High Life Experiences Survey. A total of 110 adolescent outpatients (88 females, 22 males, mean age = 16.21 years, SD = 1.38) suffering from SB were included in the study. All diagnoses including SB were confirmed by the MINI Plus Mini International Neuropsychiatric Interview. K means clustering was used to compare variances of 19 variables to decide which ones are the major criteria for assigning subjects to clusters, and principal component analysis was utilized to identify background SB dimensions in the patient sample. RESULTS: The cluster analysis identified 3 homogenous clusters differentiating suicidal adolescents characteristically: 'stress-laden/medium depressive', 'low depressive/low achievement' and 'high depressive' cluster groups. While cluster analysis confirmed the role of the severity of depression only, principal component analysis explored the following 4 underlying profiles of SB: stress-laden, dysfunctional, maladaptive and depressive/risky factors. CONCLUSIONS: Although important coping qualities failed to register as major criteria in the development of separate groups of suicidal adolescent outpatients, distinct background profiles of SB among Hungarian adolescents were found covering the risk groups according to clinical experience. Future research is warranted to identify possible variation in the coping strategies among different adolescent suicidal samples.

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)0.000
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 score0.016
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.317
Teacher spread0.287 · 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

Citations15
Published2009
Admission routes1
Has abstractyes

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