Reflections on Distributive Leadership for Work-Based Mobile Learning of Canadian Registered Nurses
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
The ubiquity, flexibility, and accessibility of mobile devices can transform how registered nurses in Canada learn beyond the confines of traditional education/training boundaries in their work settings. Many Canadian registered nurses have actively embraced mobile technologies for their work-based learning to meet their competency requirements for professional nursing practice. As self-directed learners, they are using these learning tools at point-of-need to access rich online healthcare resources, collaborate, and share information within their communities of practices. Yet, paradoxically, there are Canadian healthcare organizations that have not embraced work-based mobile learning and their contextual factors constrain and/or impede registered nurses' learning. Therefore, the goal of this reflective paper is to stimulate discussion on distributive leadership strategies for embedding this pedagogical mode of learning into Canadian healthcare workplaces for registered nurses' ongoing skills and continuing professional development.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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