Physician-Suggested Innovative Methods for Health System Resilience amidst Workforce Emigration and Sociopolitical Unrest in Nigeria: A Survey-Based Study
Bibliographic record
Abstract
<strong>Introduction:</strong> Physician emigration (the <em>brain drain</em>) and sociopolitical unrest significantly contribute to the instability of many low- and middle-income countries’ healthcare systems. However, limited literature captures the <em>locally driven</em> and <em>context specific</em> suggestions to promote and sustain these health systems’ resilience. Thus, the purpose of this study is to 1) understand the effects of physician emigration and sociopolitical unrest on Nigeria’s healthcare system, and to 2) synthesize solutions suggested by Nigeria-trained physicians in the form of a resilience framework. <strong>Methods: </strong>An anonymous online survey was conducted among Nigeria-trained physicians. Respondents were recruited using convenience and snowball sampling methods via a <em>WhatsApp</em> group for Nigeria-trained doctors. Quantitative data were analyzed using <em>Stata 17</em> and qualitative themes were coded by two independent researchers. <strong>Results:</strong> The final sample included 49 Nigeria-trained physicians—35 physicians practicing in Nigeria and 14 emigrated physicians. All of the physicians currently practicing in Nigeria have considered emigrating, with 79% of them having concrete plans to emigrate in the next five years. Among emigrated physicians, factors such as remuneration (92%) and socioeconomic state of the country (92%) contributed to their decision to emigrate. Suggestions to enhance health system resilience fell into six broad themes: 1) policy and politics, 2) funding and resources, 3) organization and structure, 4) training and education, 5) research and primary health, and 6) health for peace initiatives. <strong>Conclusions: </strong>The healthcare system is currently unstable and vulnerable due to physician emigration and sociopolitical unrest. To develop and implement solutions to mitigate these issues, capturing the locally trained physicians’ perspectives are critical. While each country’s healthcare system is unique, countries with similar strains can adapt this model for resilience building. Future studies should focus on adapting the model in different countries with policy-level action points.
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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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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".