Pharmacology revealed‐ an enhanced e‐book for midwifery education
Bibliographic record
Abstract
Educational resources in pharmacology for programs with a limited scope of practice and a small armamentarium have historically been poor because of the prohibitive cost of publishing high quality, small run textbooks. Electronic publishing allows for the development of inexpensive, sophisticated materials that meet these specific educational needs. E‐books are particularly well suited for pharmacology education because they allow for animations of dynamic processes inherent to pharmacokinetics. In our text, Pharmacology Revealed, animations were blended with immersive case studies developed for the project, computer‐based testing, a pop‐up glossary and links to other e‐resources. Course lecture recordings were transcribed to form the textual foundation of the e‐book while the PowerPoint files served as the basis for animations. The digital layout was accomplished with InDesign and published in HTML 5 as well as in an interactive Adobe PDF file. This fully functional midwifery pharmacology e‐book was developed in eight months and piloted in September 2012. Student evaluation was performed at the end of the course to obtain feedback on the content, layout and functionality. Research focused on learner performance, including knowledge acquisition. The feasibility and educational impact was evaluated using mixed methods research which included a quantitative questionnaire and a qualitative focus group.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.024 |
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".