MétaCan
Menu
Back to cohort

Interdisciplinary Perspectives on Well-being and Intervention Strategies across Life Stages

2024· article· en· W4396533221 on OpenAlexaff
Shokouh Navabinejad

Bibliographic record

VenueKMAN Counseling and Psychology Nexus · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)PsychologyEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

The discourse surrounding well-being and the myriad intervention strategies designed to enhance life satisfaction and mental health spans a wide array of disciplinary boundaries and life stages. This letter seeks address the significant contributions and interdisciplinary perspectives that inform our understanding of well-being, drawing upon recent scholarly works that collectively underscore the complexity and richness of this field. Collectively, such scholarly works underscore the importance of adopting an interdisciplinary lens in the study and application of well-being interventions. From the nuanced needs of children living under the shadow of political unrest to the complex dynamics of caregiving and the influence of digital technologies, it is evident that well-being is a multifaceted phenomenon that requires a diverse array of strategies and perspectives to address effectively. Moreover, the exploration of mindfulness as a potent intervention strategy reaffirms the value of integrating psychological and behavioral science insights into the fabric of mental health care. As we continue to navigate the challenges and opportunities presented by our evolving understanding of well-being, it becomes increasingly clear that an interdisciplinary approach is not merely beneficial but essential. By fostering collaboration across disciplines, we can enhance our collective capacity to develop and implement effective intervention strategies that cater to the diverse needs of individuals across all life stages.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.481
Teacher spread0.445 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueKMAN Counseling and Psychology NexusSame topicHealth, psychology, and well-beingFrench-language works237,207