Social networks and skilled health worker migration in Nigeria: An ego network analysis
Bibliographic record
Abstract
BACKGROUND: Nigeria provides a good case study for researchers, activists, and governments seeking to understand how social networks can help mitigate the negative impact of skilled health worker (SHW) migration in low and middle-income countries. This study aimed to map the social networks of SHWs and explore how they influence migration intentions. METHODS: We combined semi-structured qualitative interviews with an ego-network analysis of 22 SHWs living in Nigeria, used R-Studio to display and visualise their networks, and NVivo for thematic analysis of transcribed interviews. RESULTS: The network size and frequency of interaction were smaller among SHWs seeking to remain in Nigeria, however when compared to SHWs seeking to migrate, they had ties with a diverse group of stakeholders interested in improving health services. The influence of social networks on SHW migration intentions was observed within the following themes: access to information on migration opportunities, modelling of migration behaviour, support for decision making, and opportunities for policy engagement. CONCLUSION: The social networks of SHWs can aid the diffusion of norms that are relevant for improving SHW migration governance. Through their social networks, SHWs can improve awareness of the challenges associated with SHW migration among state actors and the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 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 | 0.002 | 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".