The Disruptive Consequences of Discourse Fragmentation in the Organization and Delivery of Health Care: A Look Into Diabetes
Bibliographic record
Abstract
The aim of this study is to contribute to a better understanding about how discourse fragmentation is affecting the way doctors perceive the patient's role and expectations that are being redefined under the influence of media and other information sources. The diabetes case provides the empirical evidence to support the fragmentation thesis. This condition offers a unique mix of complexity, scope, and controversy to understand the dialectics of discourse fragmentation. Through a combined analysis of media discourse and experts' discourse (researchers and clinicians), this article describes the connections between the macro (the realm of the public sphere) and the micro (the localized medical practice) in the context of health care delivery. The study concludes that a fragmented media discourse tends at the same time to nourish the public perception about the "diabetes complexities" (a multifaceted and growing epidemic), and to normalize some emerging concepts such as "prediabetes" and metabolic syndrome. This fragmentation seems to have a double-edged sword effect on doctor-patient relationships; in some occasions the atomized discourse about diabetes has a clear disruptive impact on their medical practice, adding an "extra burden" to the disease management, while in other opportunities it has a more convergent effect facilitating the dialogue and the interaction between the actors.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".