Nikolai Czajkowski
Biographic Data
| ID | 6033746 |
|---|---|
| NAME | Nikolai Czajkowski |
| GIVEN NAMES | Nikolai |
| FAMILY NAME | Czajkowski |
| SIGNATURE | CZAJKOWSKI N |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Longitudinal recovery and self-efficacy in first-episode schizophrenia
A significant proportion of first-episode schizophrenia patients can achieve clinical recovery. While these positive outcomes are noteworthy, it is important to recognize that recovery paths can vary widely among individuals. Since people with schizophrenia are concerned about their chances of recovery, the results must be shared with patients and their families. While the causal relationship between self-efficacy and recovery remains unclear, th…
Reconsidering False Positives in Machine Learning Binary Classification Models of Suicidal Behavior
We posit the hypothesis that False Positive cases (FP) in machine learning classification models of suicidal behavior are at risk of suicidal behavior and should not be seen as sheer classification error. We trained an XGBoost classification model using survey data from 173,663 Norwegian adolescents and compared the classification groups for several suicide-related mental health indicators, such as depression, anxiety, psychological distress, and…
Unveiling Adolescent Suicidality
Adolescent suicide attempts are on the rise, presenting a significant public health concern. Recent research aimed at improving risk assessment for adolescent suicide attempts has turned to machine learning. But no studies to date have examined the performance of stacked ensemble algorithms, which are more suitable for low-prevalence conditions. The existing machine learning-based research also lacks population-representative samples, overlooks p…
Does low alcohol use increase the risk of sickness absence? A discordant twin study
The increased level of sick leave observed with low level drinkers seems to be mainly explained by confounding from genetic factors. In all observational studies of the relationship between alcohol consumption and health, one should be aware that important genetic confounders are likely to influence the results
Socioeconomic status and sick leave granted for mental and somatic disorders
Low SES is associated with a higher level of sick leave granted for both mental and somatic disorders among young adults, but these associations are confounded by factors that are common to co-twins. Education and income are therefore not likely to strongly affect sick leave in young adulthood
Unveiling Adolescent Suicidality
Adolescent suicide attempts are on the rise, presenting a significant public health concern. Recent research aimed at improving risk assessment for adolescent suicide attempts has turned to machine learning. But no studies to date have examined the performance of stacked ensemble algorithms, which are more suitable for low-prevalence conditions. The existing machine learning-based research also lacks population-representative samples, overlooks p…
Socioeconomic status and sick leave granted for mental and somatic disorders
Low SES is associated with a higher level of sick leave granted for both mental and somatic disorders among young adults, but these associations are confounded by factors that are common to co-twins. Education and income are therefore not likely to strongly affect sick leave in young adulthood
Does low alcohol use increase the risk of sickness absence? A discordant twin study
The increased level of sick leave observed with low level drinkers seems to be mainly explained by confounding from genetic factors. In all observational studies of the relationship between alcohol consumption and health, one should be aware that important genetic confounders are likely to influence the results
Reconsidering False Positives in Machine Learning Binary Classification Models of Suicidal Behavior
We posit the hypothesis that False Positive cases (FP) in machine learning classification models of suicidal behavior are at risk of suicidal behavior and should not be seen as sheer classification error. We trained an XGBoost classification model using survey data from 173,663 Norwegian adolescents and compared the classification groups for several suicide-related mental health indicators, such as depression, anxiety, psychological distress, and…
Unveiling Adolescent Suicidality
Adolescent suicide attempts are on the rise, presenting a significant public health concern. Recent research aimed at improving risk assessment for adolescent suicide attempts has turned to machine learning. But no studies to date have examined the performance of stacked ensemble algorithms, which are more suitable for low-prevalence conditions. The existing machine learning-based research also lacks population-representative samples, overlooks p…
Longitudinal recovery and self-efficacy in first-episode schizophrenia
A significant proportion of first-episode schizophrenia patients can achieve clinical recovery. While these positive outcomes are noteworthy, it is important to recognize that recovery paths can vary widely among individuals. Since people with schizophrenia are concerned about their chances of recovery, the results must be shared with patients and their families. While the causal relationship between self-efficacy and recovery remains unclear, th…
Medicine (5 works) · Clinical Psychology (3 works) · Psychiatry (3 works) · Psychology (3 works) · Artificial Intelligence (2 works) · Biostatistics (2 works) · Clinical Psychology (2 works) · Demography (2 works) · Demography (2 works) · Environmental health (2 works)