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Record W4284890078 · doi:10.1080/17439760.2022.2093784

A systems perspective on human flourishing: Exploring cross-country similarities and differences of a multisystemic flourishing network

2022· article· en· W4284890078 on OpenAlexaff
Jan Höltge, Richard G. Cowden, Matthew T. Lee, Andrea Ortega Bechara, Shaun Joynt, Shanmukh V. Kamble, V. V. Khalanskyi, Liudmyla Shtanko, Ni Made Taganing Kurniati, S. Tymchenko, Vitaliy L. Voytenko, Eileen McNeely, Tyler J. VanderWeele

Bibliographic record

VenueThe Journal of Positive Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFlourishingPerspective (graphical)PsychologySocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

A systems perspective explains dynamics of human flourishing based on the relations between its constituents. Using cross-sectional data from emerging adults (ages 18–29) in 10 countries (N = 7221), this study explored the interrelatedness among constituents of flourishing – happiness & satisfaction with life, mental & physical health, meaning & purpose, character & virtue, close social relationships, and financial & material stability – within and across countries. Each country’s sample was characterized by a unique flourishing network, although there were similarities. Except for financial & material stability, all constituents were positively related across samples. Financial & material stability showed the highest cross-country heterogeneity in its relations. Happiness & satisfaction with life and meaning & purpose showed the strongest interrelations. A higher level of one constituent was associated with lower network connectivity. This systems perspective extends existing knowledge about the conceptualization of flourishing and how people can be supported to achieve and maintain complete well-being.

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)0.002
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0000.003
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 head0.195
GPT teacher head0.479
Teacher spread0.284 · 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".

Quick stats

Citations68
Published2022
Admission routes1
Has abstractyes

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