Estimating nurse migration from the Bahamas between 1994 and 2005: An exploratory descriptive study using a social network identification methodology
Bibliographic record
Abstract
The purpose of this study was to collect primary data on the migration patterns of Bahamian nurses who had registered as nurses during the period 1994-2005. We used an established social network identification method developed by Delanyo Dovlo to determine whether (and to where) Bahamian nurses had migrated. We reviewed nursing registrants' records from the Nursing Council of the Commonwealth of the Bahamas. We telephoned two nurses per cohort of registration and graduation year, asking the interviewee to identify the current location of colleagues registered in their same cohort. Between 1994 and 2005, a total of 18 out of 282 nurses were either confirmed or probably migrated (6%). Ninety-six per cent of those nurses registered during the study time frame were located during the exercise, partially because of an improvement on the Dovlo method--calling a nurse leader who could locate nursing classmates and colleagues beyond his or her own year of graduation. Nurse migration in the Bahamas appears lower than in surrounding countries, posing a research opportunity to investigate the causes for this positive deviation. Future studies employing this method should include interviews of nurse leaders who can confirm the location of a much wider range of people than can the average participant.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".