Sympatiaa Kreivi Draculalle : Elokuvat sosionomin työvälineenä

Tämän opinnäytetyön tarkoituksena oli tarkastella elokuvaa hyvinvoinnin edistäjänä ja sosionomin työvälineenä. Työssä tutustuttiin elokuvanlukutaitoon, joka on merkityksellinen taito elokuvan kanssa työskenneltäessä. Lisäksi tutkittiin elokuvakasvatusta mediakasvatuksen ja taidekasvatuksen näkökulmista sekä elokuvaterapiaa. Elokuvakasvatus ja elokuvaterapia ovat molemmat sovelluksia elokuvan käytöstä ihmisen hyvinvoinnin tukena. Näiden avulla pohdittiin elokuvankatselun ja elokuvanteon mahdollisuuksia sosionomin vuorovaikutustyön välineinä. Tarkastelussa otettiin erikseen huomioon lapsi- ja aikuisasiakkaat, sekä hahmoteltiin näille ryhmille mahdollisesti sopivia tapoja elokuvatyöskentelyyn. Elokuvia pohdittiin myös ammatillisen kasvun ja vaikuttajuuden välineenä. Työhön kuuluu lisäksi laadullinen havainnointitutkimus, joka toteutettiin vuoden 2015 Tampere Film Festivalilla. Tutkimukseen valittiin neljä näytöstä, jotka sisälsivät nuorille suunnattuja ja nuorten tekemiä elokuvia. Tutkimuksessa kysyttiin, minkälaisia teemoja kyseisten näytösten elokuvissa käsitellään ja miten näitä elokuvia voisi hyödyntää sosionomin työssä. Tutkimustuloksista kävi ilmi, että tutkimuksen elokuvissa käsitellään maailmaa laajasti yksilöiden sosiaalisista suhteista aina yhteiskunnan epäkohtien pohtimiseen saakka, aina kunkin näytöksen elokuvantekijöiden perspektiivistä. Kyseisiä elokuvia voi hyödyntää sosionomin työssä muun muassa nuorten kanssa katsellen ja teemoista keskustellen sekä hakien ideoita ja näkökulmaa elokuvantekotyöskentelyyn. Elokuvat sopivat katsottavaksi myös aikuisryhmille: niiden avulla voi tehdä esimerkiksi muistelutyötä omaan nuoruuteen liittyen tai lisätä ymmärrystä tämän päivän nuoria, kuten omia lapsiaan, kohtaan. Lisäksi elokuvat sopivat sosionomin ammatillisen kasvun välineeksi, auttaen ymmärtämään muun muassa lastenkodissa kasvaneiden nuorten kokemuksia ja huono-osaisuutta yleensäkin. Tutkimuksen perusteella voi todeta, että elokuvalla on paljon potentiaalia sosionomin työvälineenä. Jatkotutkimuksen kohteeksi ehdotetaan valitun asiakasryhmän kanssa toteutettavia käytännön sovelluksia, jotka sisältävät suunnittelun, toteutuksen sekä arvioinnin. Yhteistyönä TAMKin sosiaalialan ja media-alan kesken voitaisiin toteuttaa erilaisia audiovisuaalisia projekteja hyvinvoinnin edistämiseksi. Työ on myös monipuolistanut opinnäytetyön tekijän tapaa katsoa elokuvia.The purpose of this thesis was to study the possibilities of using film as a tool in the work of a Bachelor of Social Services and to comprise a toolkit. The theoretical section familiarizes the reader with film literacy along with film education and film therapy. Based on these two applications of film adapted to promote wellbeing of human, ideas of using film with different customer groups and in the professional growth process of a Bachelor of Social Services are introduced in the text. Both watching and making films are considered. A qualitative study was conducted at Tampere Film Festival 2015 by choosing four screenings of films aimed for and/or made by youngsters. The objective of the study was to find out what kind of themes the films deal with and whether the films could in some way contribute to the work of a Bachelor of Social Services. An observation form was created to help observation in the screening situation. The observation results about the themes were written out and compiled as larger unities. The results suggest that the films comprised of the whole human life. From small topics about social relations to wide societal problems, the films portrayed life both complex and versatile. The films can contribute in various ways to the work of a Bachelor of Social Services

Classification Tree Based Algorithms in Studying Predictors for Long-Term Unemployment in Early Adulthood : An Exploratory Analysis Combining Supervised Machine Learning and Administrative Register Data

Unemployment at young age is a negative life event that has been found to have scarring effects for future life outcomes, especially when continuing long-term. Understanding precursors for long-term unemployment in early adulthood is important to be able to target policy interventions in critical junctures in the life course. Paths to unemployment are complex and a comprehensive outlook on the most important factors and mechanisms is difficult to obtain. This study proposes a data-driven, exploratory approach for studying individual and family level factors during ages 0-24, that predict long-term unemployment at the age of 25-30. A supervised machine learning approach was applied to understand associations deriving from longitudinal, individual-level administrative data from a full birth cohort in Finland. The data comprise information about physical and social wellbeing, life course events, as well as demographics, including the parents of the cohort members. Potential predictors were chosen from the data based on theories and previous research, and used to train a model aiming to correctly classify unemployed individuals. A CART algorithm was used to build a classification tree that reveals important variables, ranges of them as well as combinations of factors that together are predictive of long-term unemployment. A random forest algorithm was used to build several trees producing smoothed predictions that reduce overfitting of one tree. CARTs and random forest models were compared to each other to understand how they perform in a research task predicting life outcomes. Both individual and family level factors were found to be predictive of the outcome. Combinations of variables such as GPA lower than ~7.5, ego’s low education level, late work history start, depressive disorders and low parental education and income levels were found to be particularly predictive of unemployment. CART models correctly classified up to 87% of the unemployed, while misclassifying 70% of the employed and having 45% overall accuracy. Testing for CART model stability, finding consistency across several tree models improved robustness. Random forest correctly predicted up to 59% of the unemployed, while also correctly classifying 65% of the employed and producing robust results. The two algorithms together provided valuable insight for better understanding factors contributing to unemployment. The study shows promise for classification tree based methods in studying life course and life outcomes

Classification Tree Based Algorithms in Studying Predictors for Long-Term Unemployment in Early Adulthood : An Exploratory Analysis Combining Supervised Machine Learning and Administrative Register Data

Unemployment at young age is a negative life event that has been found to have scarring effects for future life outcomes, especially when continuing long-term. Understanding precursors for long-term unemployment in early adulthood is important to be able to target policy interventions in critical junctures in the life course. Paths to unemployment are complex and a comprehensive outlook on the most important factors and mechanisms is difficult to obtain. This study proposes a data-driven, exploratory approach for studying individual and family level factors during ages 0-24, that predict long-term unemployment at the age of 25-30. A supervised machine learning approach was applied to understand associations deriving from longitudinal, individual-level administrative data from a full birth cohort in Finland. The data comprise information about physical and social wellbeing, life course events, as well as demographics, including the parents of the cohort members. Potential predictors were chosen from the data based on theories and previous research, and used to train a model aiming to correctly classify unemployed individuals. A CART algorithm was used to build a classification tree that reveals important variables, ranges of them as well as combinations of factors that together are predictive of long-term unemployment. A random forest algorithm was used to build several trees producing smoothed predictions that reduce overfitting of one tree. CARTs and random forest models were compared to each other to understand how they perform in a research task predicting life outcomes. Both individual and family level factors were found to be predictive of the outcome. Combinations of variables such as GPA lower than ~7.5, ego’s low education level, late work history start, depressive disorders and low parental education and income levels were found to be particularly predictive of unemployment. CART models correctly classified up to 87% of the unemployed, while misclassifying 70% of the employed and having 45% overall accuracy. Testing for CART model stability, finding consistency across several tree models improved robustness. Random forest correctly predicted up to 59% of the unemployed, while also correctly classifying 65% of the employed and producing robust results. The two algorithms together provided valuable insight for better understanding factors contributing to unemployment. The study shows promise for classification tree based methods in studying life course and life outcomes

Classification Tree Based Algorithms in Studying Predictors for Long-Term Unemployment in Early Adulthood : An Exploratory Analysis Combining Supervised Machine Learning and Administrative Register Data

Unemployment at young age is a negative life event that has been found to have scarring effects for future life outcomes, especially when continuing long-term. Understanding precursors for long-term unemployment in early adulthood is important to be able to target policy interventions in critical junctures in the life course. Paths to unemployment are complex and a comprehensive outlook on the most important factors and mechanisms is difficult to obtain. This study proposes a data-driven, exploratory approach for studying individual and family level factors during ages 0-24, that predict long-term unemployment at the age of 25-30. A supervised machine learning approach was applied to understand associations deriving from longitudinal, individual-level administrative data from a full birth cohort in Finland. The data comprise information about physical and social wellbeing, life course events, as well as demographics, including the parents of the cohort members. Potential predictors were chosen from the data based on theories and previous research, and used to train a model aiming to correctly classify unemployed individuals. A CART algorithm was used to build a classification tree that reveals important variables, ranges of them as well as combinations of factors that together are predictive of long-term unemployment. A random forest algorithm was used to build several trees producing smoothed predictions that reduce overfitting of one tree. CARTs and random forest models were compared to each other to understand how they perform in a research task predicting life outcomes. Both individual and family level factors were found to be predictive of the outcome. Combinations of variables such as GPA lower than ~7.5, ego’s low education level, late work history start, depressive disorders and low parental education and income levels were found to be particularly predictive of unemployment. CART models correctly classified up to 87% of the unemployed, while misclassifying 70% of the employed and having 45% overall accuracy. Testing for CART model stability, finding consistency across several tree models improved robustness. Random forest correctly predicted up to 59% of the unemployed, while also correctly classifying 65% of the employed and producing robust results. The two algorithms together provided valuable insight for better understanding factors contributing to unemployment. The study shows promise for classification tree based methods in studying life course and life outcomes

Classification Tree Based Algorithms in Studying Predictors for Long-Term Unemployment in Early Adulthood : An Exploratory Analysis Combining Supervised Machine Learning and Administrative Register Data

Unemployment at young age is a negative life event that has been found to have scarring effects for future life outcomes, especially when continuing long-term. Understanding precursors for long-term unemployment in early adulthood is important to be able to target policy interventions in critical junctures in the life course. Paths to unemployment are complex and a comprehensive outlook on the most important factors and mechanisms is difficult to obtain. This study proposes a data-driven, exploratory approach for studying individual and family level factors during ages 0-24, that predict long-term unemployment at the age of 25-30. A supervised machine learning approach was applied to understand associations deriving from longitudinal, individual-level administrative data from a full birth cohort in Finland. The data comprise information about physical and social wellbeing, life course events, as well as demographics, including the parents of the cohort members. Potential predictors were chosen from the data based on theories and previous research, and used to train a model aiming to correctly classify unemployed individuals. A CART algorithm was used to build a classification tree that reveals important variables, ranges of them as well as combinations of factors that together are predictive of long-term unemployment. A random forest algorithm was used to build several trees producing smoothed predictions that reduce overfitting of one tree. CARTs and random forest models were compared to each other to understand how they perform in a research task predicting life outcomes. Both individual and family level factors were found to be predictive of the outcome. Combinations of variables such as GPA lower than ~7.5, ego’s low education level, late work history start, depressive disorders and low parental education and income levels were found to be particularly predictive of unemployment. CART models correctly classified up to 87% of the unemployed, while misclassifying 70% of the employed and having 45% overall accuracy. Testing for CART model stability, finding consistency across several tree models improved robustness. Random forest correctly predicted up to 59% of the unemployed, while also correctly classifying 65% of the employed and producing robust results. The two algorithms together provided valuable insight for better understanding factors contributing to unemployment. The study shows promise for classification tree based methods in studying life course and life outcomes

The Stockholm life-course project: investigating offending and non-lethal severe violent victimization

Much is known about the patterning of offending throughout life, but less about the patterning of victimization. In this study, we used data from the Stockholm Life-Course Project (SLCP), a longitudinal study that includes measures of childhood problem behaviour. We analysed offending (criminal conviction and police suspicion), inpatient hospitalization and outpatient care for violent victimization. We replicated the well-established age-crime curve amongst SLCP study members. We found that hospitalization for severe violent victimization was most likely to occur between 20 and 40 years of age. We additionally considered how childhood problem behaviour impacted overall risk and life-course patterning of offending and victimization. Childhood problem behaviour was associated with a greater risk of criminal conviction. But childhood problem behaviour showed inconsistent associations with risk for police suspicion. Childhood problem behaviour was generally associated with greater involvement in crime up to middle adulthood. Childhood problem behaviour was generally associated with a greater risk of victimization. However, we were limited in our ability to estimate the effect of childhood problem behaviour on life-course patterning of victimization due to the rarity of victimization. These results imply a need for larger studies on violent victimization and greater nuance in our understanding of childhood risks and their life-long outcomes