Opinions and trends in biomaterials education: Report of a 2003 Society for Biomaterials survey
Bibliographic record
Abstract
The Society for Biomaterials (SFB) aims to serve its members through acting as a forum for the exchange of information and ideas. To aid in the practical development of the SFB and more specifically biomaterials education, all active, associate, and student members were surveyed. In general, the survey asked questions regarding respondent demographics, experiences and activities with the SFB, and opinions about biomaterials education. Perceptions and needs of biomaterials-related education and career-related training practices were a specific focus of the survey. A total of 140 individuals responded to the survey for a response rate of 18%. Members from industry felt that new hires, in general, should be better trained in product development, regulatory issues for new materials and devices, and in the relevant testing required. When asked what was missing from their professional education, many respondents commented that business training in areas such as negotiations, management, and understanding the needs outside of academia was lacking. Also, many respondents seemed to have trouble identifying with what they were supposed to know and felt a "lack of set professional knowledge." This study has raised many ideas and questions that require further discussion. The results should ultimately be useful for helping the SFB decide how best to focus future efforts in biomaterials education.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".