Psychosocial Factors, Pro-Inflammatory Markers & Serum Lipids among Patients with Suicidal Ideation & Suicidal Attempts
Notice bibliographique
Résumé
Background: Suicide is a worldwide serious problem. It is a leading cause of death, accounting for 1.3% of all deaths worldwide in 2019 and Approximately 703,000 people die by suicide every year. We aimed to study the personal, cognitive, psychosocial, personality traits, psychopathological, pro-inflammatory markers & serum lipids differences between patients with suicidal ideation and patients with suicidal attempts and to assess predictor factors that make suicidal ideation convert to suicidal attempt. Subjects and Methods: This cross-sectional case control study was carried on participant aged 18 – 50 years, both sexes. Suicidal behavior of the study sample was assessed using Beck Scale for Suicidal Ideation (BSSI) & participants were classified into three equal groups according to their score in BSSI and their history of recent suicidal attempt (suicidal ideation group, suicidal attempt group & control group). Participants’ sociodemographic data were evaluated by a questionnaire designed by the researcher and reviewed by experts. Psychiatric assessment was done using the Mini-International Neuropsychiatric Interview (MINI). Socioeconomic status & cognitive functions were assessed by the socioeconomic status scale for health research in Egypt & the Montreal Cognitive Assessment (MoCA) test, respectively. Personality was assessed by short form of EPQ-R (Revised Eysenck Personality Questionnaire). Assessment of impulsivity & stress were done by using the Barratt Impulsiveness Scale (BIS-11) & the Hassles and Uplifts Scale (HUS), respectively. Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A) & the Positive and Negative Syndrome Scale (PANSS) were applied to determine the symptom severity in those diagnosed with depressive, anxiety & psychotic disorders. Results: Suicidal ideations & attempts were more common among psychiatric patients especially those suffering from depression. Suicidal behavior was associated with increased severity of depressive & anxiety symptoms. The most common method of suicidal attempts among females was drug overdose, while males used self-poisoning as the most popular method in attempting suicide. Risk of suicidal ideation & attempts increased in the following situations: younger age groups especially below 35 years, female gender, substance abuse, low socioeconomic level, past history & family history of suicidal behavior, neuroticism, psychoticism & introversion personality traits, impulsivity, high daily stress levels, increase in the severity of depressive & anxiety disorders, elevated [ESR, Hs-CRP & IL-6], lowered [total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL) & triglycerides (TG)]. The following factors protected from suicidal behaviors: marriage, work and having jobs, Family and social support, playing sports, having hobbies & higher socioeconomic levels. Risk factors for transition of participants from suicidal ideation to suicidal attempt included female gender, being unmarried & unemployed, low socioeconomic level, poor social support, past history of suicidal behavior, neuroticism personality traits, impulsivity, high stress levels, depressive disorders, elevated [Hs-CRP & IL-6], and low [TC, LDL & TG]. Protective factors against transition from suicidal ideation to attempt included playing sports & extraversion personality traits. Conclusion: The problem of suicide is a frequent and a multifaceted problem among the general population.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».