Is the Whole Greater Than the Sum of Its Parts? Self-Rated Health and Transdisciplinarity
Bibliographic record
Abstract
How individuals rate their general health – self-rated health, SRH – predicts future morbidity and mortality. Evidence shows that this prospective association hold true across age groups, patient populations and ethnicities, and is independent of existing illness or biomedical conditions. The reason why such subjective self-perception of one’s health (the whole) is a valid and powerful predictor of health outcomes, beyond traditional disease biomarkers and risk factors (the parts), has remained unclear. One possibility is that the experience of Health transcends the biological domain, and that psychological, social, behavioral and spiritual factors are integrated in unique ways among each individual/patient to shape “true” health. Each domain bears different relative importance for different individuals. Thus, self-rated health, by virtue that it arises from a completely non-leading and non-directed question, may capture an emergent holistic experience that best represents health, and which translates more directly than other focused assessments into healthspan and lifespan. By reviewing epidemiological, clinical and qualitative research findings about self-rated health, this presentation will adopt a transdisciplinary stance to explore new knowledge that can be derived from the study of self-rated health, as well as its limitations. We will also discuss a practical approach to “profile” self-rated health as a means to identify therapeutic windows and orient person-centered care. Integrating individuals’ self perceptions of health into healthcare practice should enhance patient satisfaction with care, strengthen the therapeutic alliance, and promote empowerment and sustainable care.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".