Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.080 | 0.020 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".