Emigration of Skilled Healthcare Workers from Developing Countries: Can Team-based Healthcare Practice Fill the Gaps in Maternal, Newborn and Child Healthcare Delivery?
Bibliographic record
Abstract
BACKGROUND AND INTRODUCTION: Emigration of healthcare workers from developing countries is on the rise and there is an urgent need for policies that increase access to and continuity of healthcare. In this commentary, we highlight some of the negative impacts of emigration on maternal and child health and discuss whether team-based healthcare delivery could possibly mitigate the shortfall of maternal and child health professionals in developing countries. METHODOLOGY: We cross-examine the availability of supporting structures to implement team-based maternal and child healthcare delivery in developing countries. We briefly discuss three key supporting structures: culture of sharing, telecommunication, and inter-professional education. Supporting structures are examined at system, organizational and individual levels. We argue that the culture of sharing, limited barriers to inter-professional education and increasing access to telecommunication will be advantageous to implementing team-based healthcare delivery in developing countries. CONCLUSION AND GLOBAL HEALTH IMPLICATIONS: Although most developing countries may have notable supporting structures to implement team-based healthcare delivery, the effectiveness of such models in terms of cost, time and infrastructure in resource limited settings is still to be evaluated. Hence, we call on usual stakeholders, government, regulatory colleges and professional associations in countries with longstanding emigration of maternal and child healthcare workers to invest in establishing comprehensive models needed to guide the development, implementation and evaluation of team-based maternal and child healthcare delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".