Challenges and opportunities in graduate nursing education by distributed learning in Canada and Brazil
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, the authors share their experience related to graduate nursing programs offered by distributed learning (DL) in Canada and Brazil. Although degrees offered by DL are often the subject of criticism, the authors' experience has been that learning outcomes have been very good. Nevertheless, a number of challenges and opportunities have been encountered including those associated with flexibility of the program, delivering practice courses at a distance, facilitating interaction, faculty workload and preparation and student support, Newer technologies that may assist in this effort are identified. Despite the challenges encountered, students rate the program highly and ongoing efforts are underway to ensure excellence of such flexible innovative graduate programs in nursing. The authors argue that despite the challenges, DL programs offer high quality graduate education that meets the needs of many nurses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it