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Record W6963367528 · doi:10.17605/osf.io/jrvpt

Development and validation of the Japanese version of the Auckland Individualism and Collectivism Scale: Relationship between individualism/collectivism and mental health

2024· other· en· W6963367528 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCollectivismIndividualismHarmony (color)Individualistic cultureHofstede's cultural dimensions theoryScale (ratio)Mental health

Abstract

fetched live from OpenAlex

The association between cultural factors and mental health has been reported through cross-cultural studies. Most studies addressing cultural effects on psychopathology have focused on two dimensions of cultural factors: individualism and collectivism. Individualism pertains to valuing personal independence, such as competition, uniqueness, and responsibility (Shulruf et al., 2007). On the other hand, collectivism involves valuing personal interdependence, such as advice and harmony (Shulruf et al., 2007). According to Hofstede (2010), individualistic countries include mainly Western countries, such as the United States (U.S.), Australia, the United Kingdom, Germany, Canada, the Netherlands, and New Zealand. Collectivistic countries include mainly East Asian countries, such as Japan, Korea and China. Oyserman et al. (2002) reviewed the literature of cross-cultural studies and suggested that individualism and collectivism are not two-dimensional but are divided into multiple domains for each variable. Specifically, they reported that individualism includes competition, uniqueness, and direct communication, whereas collectivism includes harmony, advice, and collective goals. Shulruf et al. (2007) developed the Auckland Individualism and Collectivism Scale (AICS), based on the components of individualism/collectivism identified by Oyserman et al. (2002). The scale has been reported to have high internal consistency, factor structure validity, and measurement invariance across cultures (Shulruf et al., 2023). The AICS has been translated in 12 different languages, including Turkey, Germany, Nepal, Portugal, China, and Italy (Shulruf et al., 2023), making it a useful assessment tool for examining cross-cultural differences. However, there is no Japanese-language version of the AICS. The development of a Japanese version of the AICS (J-AICS) would clarify the specific cultural characteristics of the Japanese and contribute to examine cultural comparisons with other countries, such as the U.S., Australia, and Germany, which are considered as individualistic countries, and China and Korea, which are collectivistic countries. Therefore, we will develop the J-AICS and examine its reliability and validity in this study. Specifically, we will examine internal consistency, factorial validity, and convergent validity. In addition, previous studies have revealed the relationships between individualism/collectivism and mental health (Germani et al., 2021; Nezlek & Humphrey, 2023). Nezlek & Humphrey (2023) reported that collectivism factors were negatively correlated with depressive symptoms and positively correlated with interpersonal well-being. However, the association between cultural factors in individualism and collectivism and mental health has not been fully examined in Japan. By examining cultural factors as measured by the J-AICS and mental health, it is possible to identify which cultural variables are associated with mental health. This finding would contribute significantly to the understanding of culture and mental health. Therefore, this study will also examine the association between cultural factors related to individualism and collectivism and variables related to mental health.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.337
Teacher spread0.295 · 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 designBench or experimental
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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Citations0
Published2024
Admission routes1
Has abstractyes

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