Bibliographic record
Abstract
Medical communicators are professionals with a knowledge of both medicine and writing who are able to deliver complex scientific information to a variety of audiences. As the leading professional organization for medical communicators with a membership of nearly 5,000, the American Medical Writers Association (AMWA) is well-situated to tap into their member network to better understand the diverse backgrounds and experience of medical communicators. In this article, AMWA presents the demographic data (eg, age, gender, education, and work experience) received from the Medical Communication Compensation Survey to create a snapshot of the medical writing community. AMWA emailed the most recent Web-based survey to medical writers and editors during the first quarter of 2019. Overall, 7,456 individuals received the survey, and 1,418 respondents completed the survey. About two-thirds (66.1%) of the respondents were employed by a company, similar to the 2015 survey (65.1%), whereas the remaining one-third were freelancers. Most respondents were female (83.4%), and the average age of all respondents was about 48 years. The average time spent working for pay as a medical communicator for all respondents was 12 years. Most respondents held a doctoral-level degree (46%) or a master’s degree (32%) as their highest level of education. Nearly half of all respondents had their highest degree in the field of science (47.2%), whereas 9% had their highest degree in English. A key takeaway from the survey is that medical communicators are a highly educated group of professionals, indicating a commitment to continuous learning. AMWA members are encouraged to keep their member profiles up to date to provide additional demographic information to support AMWA’s mission of promoting excellence in medical communication and providing educational resources in support of that goal.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.126 | 0.082 |
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".