Foreign advertisements for doctors in the SAMJ 2006 - 2010
Bibliographic record
Abstract
BACKGROUND: There is much concern about the migration of health professionals from developing countries, and the contribution of active recruitment to the phenomenon. One active recruitment strategy is advertisements in professional journals and other media. OBJECTIVE: To establish the trends in foreign advertisements for doctors placed in the South African Medical Journal (SAMJ) from January 2006 to December 2010. METHODS: A retrospective review was conducted of 60 issues of the SAMJ published in the preview years. Printed journals were scanned for foreign advertisements. The findings were compared with a review of 2000 - 2004 in the same journal. RESULTS: There were 1 176 foreign advertisements placed in the SAMJ in the review period, reducing from 355 in 2006 to 121 in 2010. The countries placing the most advertisements were Australia (n=428, 36.4%), Canada (n=286, 24.3%), New Zealand (n=191, 16.2%) and the UK (n=108, 9.2%). Compared with the earlier findings, there was a reduction in advertisements for the top countries, excepting Australia. The top 4 countries remained the same for the 2 review periods, but the order changed, with Australia superseding the UK. CONCLUSION: The number of foreign advertisements placed in the SAMJ declined over the period under review, and there was a change in ranking of the top 4 advertising countries. These findings are discussed from the perspective of global human resources for health initiatives.
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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 | 0.006 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".