From fragmentation to de--fragmentation: a critical view about the role of public relations in healthcare
Bibliographic record
Abstract
Public relations (PR) in the healthcare domain can be defined as a disruptive force that contributes to the development of fragmented perceptions of health issues, disease prevention and treatment. This paper describes the so--called fragmentation of healthcare discourses and the role that PR plays in this process, with particular focus on diabetes as an exemplary case of information atomization. The paper also proposes the main principles guiding a de--fragmentation strategy, a kind of counter--practice that returns to the normative approach of PR as a mediation function between organizations’ interests and public expectations.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.153 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".