Teaching epidemiology: Nursing student achievement in a multi-campus, multi-faculty, distance delivery course
Bibliographic record
Abstract
Background: A common epidemiology curriculum was developed by a nurse epidemiologist and delivered by generic nursing faculty on 5 campuses of a western U.S. university school of nursing. The objective was to assess whether student achievement would be affected in this multi-campus, multi-faculty, distance delivery common epidemiology course. Methods: 329 nursing students admitted to 5 campuses of one university were enrolled by cohort in an epidemiology course between 2009 and 2011. 138 students were enrolled by cohort in four courses taught by the nurse epidemiologist who developed the course. The remaining 191 students were divided into sections, enrolled in the common course, and taught by multiple nursing faculty members, variously prepared in the field of public health. Final test score averages were compared between 6 cohorts of students: 1 large set of cohorts taught by the nurse epidemiologist who developed the common course but prior to its implementation, and 5 smaller cohorts of students taught by multiple faculty (including the nurse epidemiologist (author)), at multiple campuses, using identical curriculum. Results: A moderate, but significant difference in student achievement was noted between the courses taught by the nurse epidemiologist as compared to the other cohorts. Conclusions: Critical faculty shortages and the need for updated public health nursing education call for innovative teaching approaches. A commonly developed epidemiology course can be delivered at multiple campuses by generic faculty with minor loss of student achievement.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 0.001 |
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".