Participant Perceptions of the Influences of the NLM-Sponsored Woods Hole Medical Informatics Course
Bibliographic record
Abstract
This report provides an evaluation of the National Library of Medicine-sponsored Woods Hole Medical Informatics (WHMI) course and the extent to which the objectives of the program are achieved. Two studies were conducted to examine the participants' perceptions of both the short-term (spring 2002) and the long-term influences (1993 through 2002) on knowledge, skills, and behavior. Data were collected through the use of questionnaires, semistructured telephone interviews, and participant observation methods to provide both quantitative and qualitative assessment. The participants of the spring 2002 course considered the course to be an excellent opportunity to increase their knowledge and understanding of the field of medical informatics as well as to meet and interact with other professionals in the field to establish future collaborations. Past participants remained highly satisfied with their experience at Woods Hole and its influence on their professional careers and their involvement in a broad range of activities related to medical informatics. This group considered their knowledge and understanding of medical informatics to be of greater quality, had increased their networking with other professionals, and were more confident and motivated to work in the field. Many of the participants feel and show evidence of becoming effective agents of change in their institutions in the area of medical informatics, which is one of the objectives of the program.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".