Knowledge and awareness of biosimilars and shared decision-making among gastroenterology team members in Colorado, USA
Bibliographic record
Abstract
Background: There are gaps in gastroenterologist team members’ understanding of various topics related to biosimilars. We aimed to examine perspectives, views, and attitudes toward biosimilar and shared decision-making (SDM) among gastroenterology team members in Colorado, USA. The ultimate goal was to increase knowledge and awareness of biosimilars and SDM. Research design and methods: We developed educational materials focused on biosimilars and SDM and distributed them to each participating gastroenterology office. Subsequently, we conducted a survey of all team members from participating offices. Results: Responses were obtained from 54 gastroenterology team members. Most respondents identified the correct answer regarding biosimilars, the nocebo effect, and SDM. Almost half (47.2%) of respondents scored their level of awareness regarding biosimilars prior to reading our educational materials as poor, and nearly one quarter (26.4%) indicated so for SDM. Improvement in scores after reading our materials was significant for both biosimilars and SDM (i.e. biosimilar: z = 6.276, p-value <0.001 and SDM z = 6.328, p-value <0.001). Conclusions: Educational efforts effectively increased the low baseline knowledge and awareness of biosimilars and SDM among gastroenterology team members. More educational projects focused on biosimilars and SDM are needed to reduce the nocebo effect and prevent hampering of the cost-savings of biosimilars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".