Professional skills for physiology majors: defining and refining
Bibliographic record
Abstract
Changing labor markets require a workforce that is broadly trained for a variety of possible careers. Recognizing this, government and industry representatives, along with students and their families, are encouraging universities and colleges to focus more on developing transferable skills to maximize employability of their graduates. In response, academic institutions and professional organizations have begun to develop lists of transferable professional skills that they expect students to have acquired on graduation. At the 2018 Physiology Majors Interest Group (P-MIG) meeting, participants stated that there was a need to define a list of professional skills for undergraduates completing a physiology major. To this end, a professional skills committee was established. Initially members of the committee worked together to develop a draft list of skills. An iterative process of refining the list was then undertaken through presentations/small-group discussions at appropriate international meetings and via an online survey. Over 60 physiology educators, the majority of whom teach in undergraduate programs, provided input. The final list (presented here) consists of 13 skills grouped in four broad categories: think critically, communicate effectively, behave in a socially and scientifically responsible manner, and demonstrate laboratory proficiency. It is anticipated that the list will be used for curriculum mapping and to guide the development of new physiology courses and major programs. The professional skills committee now plans to develop rubrics and tools that will allow for the assessment of these skills.
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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".