Professional explanations of disease trajectories
Bibliographic record
Abstract
Media representation of health and illness is a common pursuit of discourse analysts. Less common is the study of how healthcare professionals and researchers provide explanations about health and disease in interview situations. In this paper we focus on professional explanations concerning Type 2 Diabetes and Coronary Heart Disease which are major health problems in the Western world, consuming a significant percentage of the health budget in many countries. Efforts to halt the increasing incidence are directed, in part, to understanding the causes of both illnesses. As part of a larger project aimed at understanding causal explanations for these conditions, we interviewed 18 clinicians and researchers working in the field in the UK to determine their views on why the incidence of these conditions continues to rise worldwide. We adopt a rhetorical discourse analytic perspective to highlight the function and significance of raising these issues as causal explanations. While scientific physiological and epidemiological explanations reflecting current research featured in the interviewees’ accounts, of greater interest were the explanations of socioeconomic and political factors that contribute to the ongoing epidemic through the mediating physiological processes.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".