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The Short Depression-Happiness Scale:A Russian Translation

2017· article· en· W2622922820 on OpenAlexaboutno aff
Christopher Alan Lewis, Mikhail Khukhrin, Svetlana Galyautdinova, Sadia Musharraf, M. J. T. Lewis

Bibliographic record

VenueIndian Journal of Positive Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessPsychologyPositive psychologySocial psychologyConceptualizationScale (ratio)DienerSubjective well-beingOperationalization

Abstract

fetched live from OpenAlex

Over the last 25 years, there has been growing research interest in the field of positive psychology (Gable & Haidt, 2005; Ivtzan, Lomas, Hefieron, & Worth, 2015; Joseph, 2015; Lomas, Hefieron, & Ivtzan, 2014; Snyder & Lopez, 2009). Within contemporary positive psychology, there is a wide range of self-report instruments to measure strengths and virtues suitable for use by both researchers and clinicians (Lopez & Snyder, 2003). One area of specific interest has been the operationalization and conceptualization of the construct of happiness. Examples of self-report scales of happiness include the Memorial University of Newfoundland Scale of Happiness (Kozma & Stones, 1980); the Affectometer 2 (Kammann & Flett, 1983); the Happiness Measures (Fordyce, 1988); the Oxford Happiness Inventory (Argyle, Martin, & Crossland, 1989); the Subjective Happiness Scale (also known as the General Happiness Scale); (Lyubomirsky & Lepper, 1999); the Oxford Happiness Questionnaire (Hills & Argyle, 2002); the Subjective Fluctuating Happiness Scale (Dambrun et ah, 2012); the Subjective Authentic Durable Happiness Scale (Dambrun et ah, 2012); and the Pemberton Happiness Index (Hervas & Vazquez, 2013).One established measure of happiness is the 25-item Depression-Happiness Scale (Joseph & Lewis, 1998). The Depression-Happiness Scale consists of 13 items with negative (e.g., I felt sad) and 12 items with positive content (e.g., I felt cheerful). The positive and negative items were constructed in a way, that they should represent each other's opposites and include the same level of intensity (I felt happy vs. I felt sad). Each item is rated on a four-point scale: never (0), rarely (1 ), sometimes (2), and (3). Three scores can be calculated: One for positive feelings, one for negative feelings, and a total-score (Lewis, Joseph, & McCollam, 1996). The 13 items concerned with negative thoughts, feelings, and bodily experiences are reverse scored so that possible scores of the total scale can range from 0 to 75 and the higher the score, the happier a person is rated. Previous studies have shown good internal consistencies for the total score ranging from .85 to .95 (Lewis et ah, 1996; Lewis, McCollam, & Joseph, 2001; McGrealJ Farsi (Bayani, 2006); and German (Paulitsch, Lewis, & Hartig, 2017). There also exists a 6-item Short Depression-Happiness Scale (Joseph, Linley, Harwood, Lewis, & McCollam, 2004) for use when time or space is limited. The Short Depression-Happiness Scale has been translated into Turkish (Sapmaz & Temizel, 2013); and can also be derived from the parent Depression-Happiness Scale translations into Hindi (Kishore & Pal, 2003); Farsi (Bayani, 2006); and German (Paulitsch et al., 2017). The Short Depression-Happiness Scale has the same structure and content as the original 2 5-item but has only six items. Therefore, the total scale can range from 0 to 3 0.At present within the Russian-speaking world, there is much interest among social scientists in positive psychology (Lyubomirsky, 2014). Indeed, Russia provides an interesting context to undertake such work. For example, there are inconsistencies in findings and commentaries that Russia consistently ranks low in levels of happiness in international comparisons (Myers & Diener, 1995; Smetanina, 2011 ; Yelshansky, Anufriev, Saparin, & Semyonov, 2016).Practically for researchers and clinicians interested in positive psychology in the Russian context, there are presently a growing number of scales available in the Russian language (Yelshansky, Anufriev, Kamaletdinova, Saparin, & Semyonov, 2016a; Yelshansky, Anufriev, Kamaletdinova et al., 2016b) and these are often Russian language translations of established scales developed in other languages. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.389
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2017
Admission routes1
Has abstractyes

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