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SREIS-D
(2020)
Emotionale Intelligenz (EI) ist ein zentraler Prädiktor psychischer Gesundheit. Im deutschsprachigen Raum lag bislang keine am Vier-Facetten-Modell der EI orientierte Selbstbeschreibungsskala vor, die an klinischen und nicht-klinischen Gruppen getestet wurde. Die Self-Rated Emotional Intelligence Scale (SREIS) ist mit 19 Items ein ökonomisch einsetzbares Instrument. Die Skala wurde ins Deutsche übertragen und psychometrisch überprüft. Außerdem wurde die SREIS erstmals an einer klinischen Population getestet. Auch werden erstmals differenzierte Ergebnisse zu den vier EI-Facetten vorgelegt. Die Ergebnisse bestätigen die Faktorenstruktur der englischen Originalskala. Die Reliabilität der Gesamtskala ist als gut einzustufen. Validität wird durch erwartungskonforme Korrelationen mit anderen EI-Maßen sowie klinischen Parametern belegt. Durch Diskriminationsfähigkeit zwischen klinischer Stichprobe und nicht-klinischer Kontrollgruppe zeigt die Skala zusätzlich klinische Relevanz. Skalare Messinvarianz zwischen beiden Gruppen liegt vor. Die SREIS-D ist ein ökonomisch einsetzbares Selbstberichtsmaß zur Erfassung von Facetten der EI im klinischen und subklinischen Bereich.
Objectives:
The prevalence rates for mental health (MH) problems in cancer patients is high, although reduced uptake of services may be influenced by mental health literacy (MHL). The objective of this study was to investigate the MHL for depression and panic disorder (PD), including treatment preferences in Australian adults who had been diagnosed and treated for cancer, and whether MHL and treatment preferences was influenced by sex, age, and individuals' lived MH experience.
Method:
A total of 421 cancer survivors (n = 378 females) completed a self-report survey. Participants were asked to specify whether they had a lived experience with anxiety and/or depression, and to indicate treatment preferences for managing cancer-related distress. Two vignettes were administered to assess MHL for depression and PD.
Results:
The MHL accuracy for depression was higher than PD. Accuracy rates were higher for females with a lived experience with anxiety and/or depression; although the accuracy rate for PD was significantly lower in males. A high proportion of individuals preferred exercise and in-person counselling to manage depression and PD. Internet-based therapies were not strongly preferred for managing MH problems.
Conclusions:
The MHL for depression and PD is moderate for adult cancer survivors, with higher levels indicated for individuals with a personal lived experience with anxiety and/or depression. Public health campaigns for enhancing MHL should broaden to include individuals experiencing comorbid physical health conditions. Health providers also need to take into account client preferences for evidence-based therapies.
Labor market policy tools such as training and sanctions are commonly used to help bring workers back to work. By analogy to medical treatments, the individual exposure to these tools may have side effects. We study effects on health using individual-level population registers on labor market events outcomes, drug prescriptions and sickness absence, comparing outcomes before and after exposure to training and sanctions. We find that training improves cardiovascular and mental health and lowers sickness absence. The results suggest that this is not due to improved employment prospects but rather to instantaneous features of participation such as, perhaps, the adoption of a more rigorous daily routine. Unemployment benefits sanctions cause a short-run deterioration of mental health, possibly due higher stress levels, but this tapers out quickly.
Background:
Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models.
Methods:
For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases.
Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning
can then be deployed to classify specific behaviour and mental health patterns.
Results:
We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through
rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project.
Conclusions:
This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns
of unknown effects.