Partnerships for interdisciplinary collaborative global well‐being
Bibliographic record
Abstract
Health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity. The multifaceted intertwined nature of optimal health, mental health, and well-being requires operational, sustainable interdisciplinary partnerships in order to improve personal and global well-being and happiness. The initial step must be the assessment of the nature and magnitude of local problems in the global context. The WHO annual reports may be an adequate departure point as they can demonstrate the global nature of stressful situations and their association with physical and mental stress-related disorders. Therein, mental health professionals should spearhead change and progress. Attitudes need to be pro-active and partnerships are essential. Pertinent data should be evaluated by local experts who will determine the needs and how best to face them and achieve solutions. Hopefully, common regional denominators will lead to the formation of Regional Interdisciplinary Collaborative Alliances (RICAs) who will share needed resources and focus particularly on vulnerable populations. The RICAs would be supported by experts and technological facilities located in developed economy centers. The long-term goal is to turn the concept of pursuit of happiness into a well-perceived reality.
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.045 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".