Navigating the nexus of LMIC healthcare facilities, nurses' welfare, nurse shortage and migration to greener pastures: A narrative literature review
Bibliographic record
Abstract
Background & Aim: This review explores the intricate interplay between healthcare facilities, nurses' welfare, and the dynamics of shortage and migration in LMICs. Through a comprehensive global lens, the analysis delves into the multifaceted challenges and implications inherent in nurse migration, offering insights into developing effective strategies for a sustainable and equitable healthcare workforce. Methods & Materials: This narrative literature review was conducted from 2012 to present across PubMed, CINAHL, MEDLINE, EmCare, British Nursing Index, Hinari, APA PsycINFO, ProQuest, and EMBASE. Google Scholar was also searched for grey literature and the reference lists of the included articles were examined to identify additional relevant studies. A comprehensive analysis was conducted, employing descriptive theoretical frameworks to examine the association of nurses’ welfare, shortage, and migration with specific emphasis on elucidating its global implications. The analysis identified healthcare facilities in LMICs, nurses' welfare in LMICs and its significance, global nurse shortage and its consequence, and nurses’ migration as a response to challenges. These themes provided a framework for understanding the phenomenon under review. Results: The migration of nurses in search of greener pastures has not entirely resolved the challenges faced by both migrating nurses and the recruiting countries. The persistently unfavourable conditions in the nurses’ home countries regarded as the push factors continue to worsen while the anticipated gain referred to as pulls, in the recruiting countries are often found to be imperfect and ultimately insufficient to fulfil the expectations of the migrating nurses.Conclusion: Individual countries must internally formulate or adapt policies to address these issues, taking cognizance of the importance of a global perspective when designing interventions, to prevent inadvertently exacerbating gaps in other nations while addressing local healthcare challenges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".