Updating the descriptive biopsychosocial approach to fit into a formal person-centered dynamic coherence model - Part I: Some few basics.
Bibliographic record
Abstract
The current biopsychosocial model is predominantly descriptive and ontological semantic and formal issues need to be integrated to it in order to update this approach. Covering aspects of both human biology and human personhood requires the level of discretised facts, the level of underlying coherences and their meaning to be taken into account. For the intended and needed update to the biopsychosocial approach, the resulting model must be congruent with both science and humanities. Prevailing models of health/illness and resultant beliefs have considerable power to define which experience of sickness is valid, and who is alleviated from suffering. As a corollary, the responsibility to be as critical, and careful as possible when accepting, using, or developing, a model of human health and the development of illness is imperative. We propose that one way to address this responsibility is to formalise the most insightful and theoretically promising concept of health and the development of illness to date, George Engel’s „biopsychosocial model“ (BPS) [1]. The intention is to inform and influence healthcare research and practice towards more practicable and beneficent treatment of people within the healthcare and allied systems. The groundwork for this is presented in part I and it differentiates this paper from any other so far on the BPS. A new perspective on ancient philosophy helps to avoid separating topics that are indistinguishable, and compounding topics that should be addressed separately. Part II and part III apply this foundation to more specific issues in person-centred healthcare.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".