Mapping Nurse Mobility in Canada with GIS: Career Movements from Two Canadian Provinces
Bibliographic record
Abstract
Recent years have witnessed the publication of a growing number of studies of nursing which, from a disciplinary perspective, are geographical in their orientation. Conceptually, while the emphasis in much of this research has been focused at the micro scale on the dynamics between nursing and "place," curiously there has been scant attention to geometrical "space," and the basic yet important locational and distributive features of nursing at the macro scale. Noting this gap in the literature, the authors of this paper used a Geographical Information System (GIS) to map the movement of 199 nurses from two Canadian provinces where they were educated - Manitoba and Newfoundland - to the provinces where they currently live and work. While the findings show that nurses who move tend to move to nearby provinces, more generally they illustrate the effectiveness of GIS for managing data and representing findings from workforce studies.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".