Perhevapaat 2013 -väestökyselyn toteutus : Tiedonkeruu, aineiston edustavuus ja perustietoja vastaajista

Perhevapaat 2013 -väestökysely on Kelan tutkimusosaston ja Terveyden ja hyvinvoinnin laitoksen (THL) Politiikan seuranta- ja vaikuttavuusyksikön yhteydessä toteuttama laaja väestökysely. Kyselyn kohderyhmänä olivat vuonna 2011 lapsen saaneet äidit ja isät. Kysely oli kolmas osa vastaavien kyselyiden sarjaa, joista aiemmat on toteutettu vuosina 2001 ja 2006. Tämänkertainen kysely toteutettiin ensimmäistä kertaa täysin sähköisesti kun taas aiemmat ovat olleet perinteisiä paperikyselyitä. Kyselyn perusjoukon muodostivat vuonna 2011 lapsen saaneet äidit ja isät, jotka olivat käyttäneet vanhempainpäivärahoja eli äitiys-, isyys- tai vanhempainrahaa kyseisestä lapsesta, joille löytyi Kelaan ilmoitettu sähköpostiosoite ja jotka asuivat kyselyhetkellä Suomessa. Sähköpostiosoite löytyi kaikkiaan 75,7 prosentille etuuksia käyttäneistä äideistä ja 68,3 prosentille etuuksia käyttäneistä isistä. Otannan yhteydessä äidit jaettiin kolmeen ryhmään, perusotoksen äiteihin, monikkoäiteihin ja 18–22-vuotiaisiin nuoriin äiteihin. Perusotoksen äidit muodostavat aineiston perustan ja tästä ryhmästä tehtyyn otokseen kuului kaikkiaan 6 914 äitiä. Perusotoksen äitien lisäksi erillisaineistoja varten kyselyyn otettiin mukaan kaikki monikkoäidit ja nuorten äitien ryhmää kasvatettiin täydennysotoksilla. Kyselyyn tuli mukaan kaikkiaan 8 297 äitiä. Isien puolella isäotoksen koko oli 6 781 isää. Otos toteutettiin jakamalla isät ensin kahteen ryhmään käytettyjen perhevapaapäivien mukaan ja ottamalla näistä ositteista omat otoksensa. Äitien perusotoksessa kyselyn vastausaste oli 44,1, monikkoäideillä 38,4 ja nuorilla äideillä 34,6 prosenttia. Isien vastausaste oli 32,3 prosenttia. Äitiaineistojen edustavuus on hyvä ja sitä tullaan käyttämään varsinaisissa analyyseissä sellaisenaan, kun taas isäkyselyssä enemmän vanhempainpäivärahoja käyttäneet isät olivat aktiivisempia vastaamaan kuin vähän vanhempainpäivärahoja käyttäneet. Isäaineistossa ryhmien suhteelliset osuudet painotettiin vastaamaan perusjoukkoa. Kyselyllä saadaan paljon tietoa suomalaisista pienten lasten perheistä. Voimme kuvailla perheitä, saada tietoa syistä, miksi perheet tekivät erilaisia lapsenhoitovalintoja ja saada tietoa perheiden mielipiteistä liittyen perhevapaajärjestelmään. Esimerkiksi perusäitien kyselyssä ja isäkyselyssä saadut vastaukset olivat hyvin samankaltaisia, kun tarkasteltiin vastaajien käsitystä perheen toimeentulosta, omista tai puolison tuloista sekä perhevapaiden käytöstä. Runsaat 40 prosenttia sekä äideistä että isistä oli puolestaan sitä mieltä, että tuolloinen mahdollisuus myöhentää isäkuukautta vaikutti ainakin jonkin verran siihen, että isät käyttivät oikeuttaan. Sen sijaan isät olivat äitejä hieman useammin valmiita kehittämään perhevapaajärjestelmää. Mielipiteet, jotka koskivat päivähoito-oikeuksien rajaamista, jakautuivat työttömyystaustan mukaan. Vastaajat, joiden perheessä oli työttömyyttä, suhtautuivat muita kielteisemmin päivähoito-oikeuksien rajoittamiseen. Mielipiteisiin eri aiheista vaikuttaa niin vastaajien ja heidän perheidensä tilanne kuin heidän kokemuksensa näistä järjestelmistä. Lapsiperheet ovat erilaisia ja niiden joukossa on useita erilaisia ryhmiä. Kyselyaineistoja analysoitaessa on tärkeää huomioida nämä tekijät.2., korjattu painos (16.3.2017

Towards tunable consensus clustering for studying functional brain connectivity during affective processing

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html

From Motion to Emotion : Accelerometer Data Predict Subjective Experience of Music

Music is often discussed to be emotional because it reflects expressive movements in audible form. Thus, a valid approach to measure musical emotion could be to assess movement stimulated by music. In two experiments we evaluated the discriminative power of mobile-device generated acceleration data produced by free movement during music listening for the prediction of ratings on the Geneva Emotion Music Scales (GEMS-9). The quality of prediction for different dimensions of GEMS varied between experiments for tenderness (R12(first experiment) = 0.50, R22(second experiment) = 0.39), nostalgia (R12 = 0.42, R22 = 0.30), wonder (R12 = 0.25, R22 = 0.34), sadness (R12 = 0.24, R22 = 0.35), peacefulness (R12 = 0.20, R22 = 0.35) and joy (R12 = 0.19, R22 = 0.33) and transcendence (R12 = 0.14, R22 = 0.00). For others like power (R12 = 0.42, R22 = 0.49) and tension (R12 = 0.28, R22 = 0.27) results could be almost reproduced. Furthermore, we extracted two principle components from GEMS ratings, one representing arousal and the other one valence of the experienced feeling. Both qualities, arousal and valence, could be predicted by acceleration data, indicating, that they provide information on the quantity and quality of experience. On the one hand, these findings show how music-evoked movement patterns relate to music-evoked feelings. On the other hand, they contribute to integrate findings from the field of embodied music cognition into music recommender systems

Exploring a rationale for choosing to listen to sad music when feeling sad

Choosing to listen to self-identified sad music after experiencing negative psychological circumstances seems paradoxical given the commonly-held view that people are motivated to seek a positive affective state when distressed. We examined the motivations people described to listen to music they identified as sad, particularly when experiencing negative circumstances, and the self-reported effects of this activity. We asked adults to respond to an online survey and analyzed their narrative reports using a modified grounded theory approach. Responses were received from 65 adults across five countries. The process that underlies choosing to listen to sad music as well as the self-regulatory strategies and functions of sad music were identified. The music-selection strategies included: connection; selecting music based on memory triggers; high aesthetic value; and message communicated. The functions of these strategies were in the domains of (re-)experiencing affect, cognitive, social, retrieving memories, friend, distraction, and mood enhancement. We additionally modelled the underlying psychological process that guides sad music listening behaviour and the effects of listening. These findings present core insights into the dynamics and value of choosing to listen to self-identified sad music when coping with negative psychological circumstances

From Sound to Significance: Exploring the Mechanisms Underlying Emotional Reactions to Music

A common approach to studying emotional reactions to music is to attempt to obtain direct links between musical surface features such as tempo and a listener’s responses. however, such an analysis ultimately fails to explain why emotions are aroused in the listener. in this article we explore an alternative approach, which aims to account for musical emotions in terms of a set of psychological mechanisms that are activated by different types of information in a musical event. this approach was tested in 4 experiments that manipulated 4 mechanisms (brain stem reflex, contagion, episodic memory, musical expectancy) by selecting existing musical pieces that featured information relevant for each mechanism. the excerpts were played to 60 listeners, who were asked to rate their felt emotions on 15 scales. skin conductance levels and facial expressions were measured, and listeners reported subjective impressions of relevance to specific mechanisms. results indicated that the target mechanism conditions evoked emotions largely as predicted by a multimechanism framework and that mostly similar effects occurred across the experiments that included different pieces of music. we conclude that a satisfactory account of musical emotions requires consideration of how musical features and responses are mediated by a range of underlying mechanisms

Group Music Therapy as a Preventive Intervention for Young People at Risk: Cluster-randomized Trial

BACKGROUND: Music forms an important part of the lives and identities of adolescents and may have positive or negative mental health implications. Music therapy can be effective for mental disorders such as depression, but its preventive potential is unknown. OBJECTIVE: The aim of this study was to examine whether group music therapy (GMT) is an effective intervention for young people who may be at risk of developing mental health problems, as indicated via unhealthy music use. The main question was whether GMT can reduce unhealthy uses of music and increase potentials for healthy uses of music, compared to self-directed music listening (SDML). We were also interested in effects of GMT on depressive symptoms, psychosocial well-being, rumination, and reflection. METHODS: In an exploratory cluster-randomized trial in Australian schools, 100 students with self-reported unhealthy music use were invited to GMT (weekly sessions over 8 weeks) or SDML. Changes in the Healthy-Unhealthy Music Scale (HUMS) and mental health outcomes were measured over 3 months. RESULTS: Both interventions were well accepted. No effects were found between GMT and SDML (all p \u3e 0.05); both groups tended to show small improvements over time. Younger participants benefited more from GMT, and older ones more from SDML (p = 0.018). CONCLUSIONS: GMT was associated with similar changes as SDML. Further research is needed to improve the processes of selecting participants for targeted interventions; to determine optimal dosage; and to provide more reliable evidence of effects of music-based interventions for adolescents