Evolving a collaborative matrix for teaching informatics online to health sciences students at the Massachusetts College of Pharmacy and Health Sciences
Bibliographic record
Abstract
Introduction – The study of informatics is multidisciplinary in nature. The informatics course, HSC 310: Health Care Informatics (HSC 310), for undergraduate health sciences students at the Massachusetts College of Pharmacy and Health Sciences (MCPHS) is an example of a librarian-led, multidisciplinary team functioning within a totally online environment. Description – The development and design of the course HSC 310 is reviewed. Issues and challenges are discussed, as well as the benefits of interdisciplinary expertise in the learning environment. Outcomes – Because informatics is fundamentally interdisciplinary in nature, librarians, instructional designers, statisticians, nurses, pharmacists, and other professionals can learn from each other and strengthen the learning experience of students. The “matrix” of informatics explains how diverse types of information interact with, and impact on, each other. This knowledge is crucial to the understanding of information and its role in one's professional life. There was a journey taken in the design of the course, its evolution cumulating in its final online form. Our unique design was an informatics exercise in itself. Student feedback confirmed that, in addition to the course content, the online environment was a positive educational experience. Discussion – Librarian involvement in teaching informatics at MCPHS began with a National Library of Medicine (NLM) fellowship in informatics at Woods Hole, Massachusetts. This resulted in the library's first for-credit course offered totally in-class and included the participation of faculty from other disciplines. Successful collaboration with the College instructional designer was essential in moving this librarian-led course to a totally virtual environment. In addition, the teaming of librarians with other faculty resulted in two nursing-led, hybrid courses on our Boston and Manchester campuses. During the past two years, interdisciplinary teams have provided informatics instruction to the Boston campus within a totally online environment. This paper discusses one of those courses, HSC 310: Health Care Informatics. The topics discussed in the course and the virtual platform have both provided a valuable learning experience for faculty and students. Informatics is an area where varied disciplines can join to achieve common goals.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".