Exploring the Role of Medical and Consumer Articles in the Diffusion of Information related to Medical Change
Bibliographic record
Abstract
Using content analysis, this study provides theoretical means for reconciling social network theory focusing on human influence in information use and behavior change, and current emphasis in medical fields on the principle role of published literature in guiding and changing clinical practice. Trust enhancing features within published articles are explored.En utilisant l’analyse de contenu, cette étude présente les moyens théoriques pour relier la théorie du réseau social en convergeant sur l’influence humaine de l’utilisation de l’information et sur le changement de comportement, de même que sur l’impact courant des domaines médicaux sur le rôle principal de la littérature publiée pour guider et modifier les pratiques cliniques. Les caractéristiques facilitant la confiance à propos des articles publiés sont explorées.
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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 | 0.041 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".