Foreign-trained early-career doctors and dynamics in Nigeria: Findings from the charting studies
Bibliographic record
Abstract
Dear Editor, This letter examined the Challenges of Residency Training and early-career doctors (ECDs) in Nigeria (CHARTING) I and II data. The CHARTING studies aimed to determine the proportion of foreign-trained medical and dental graduates among ECDs in Nigeria in addition to their country of attainment of a first medical degree. The CHARTING study is the most extensive study examining demographic, workplace, and mental issues among ECDs in Nigeria to date.[1–4] The two CHARTING datasets (CHARTING I and II) showed a similar but low proportion of foreign-trained human resources among ECDs in Nigeria. Out of the 730 ECDs assessed in CHARTING-I, only 24(3.3%) were foreign-trained. Similarly, of the 633 ECDs assessed in CHARTING-II, only 50(8.0%) were foreign-trained doctors [Table 1]. Most were trained in institutions in the Middle East (Sudan and Egypt), Eastern Europe (Russia and Ukraine), the Far East (China), and the Caribbeans [Table 2].Table 1: Foreign and Nigeria-trained early-career doctors in chartıng-I and chartıng-II studiesTable 2: Country of training of foreign-trained early-career doctors in NigeriaWhile brain drain signifies loss of workforce,[5] the presence of foreign medical graduates (FMGs) seen in these datasets indicates some brain gain of about 3.3% in 2019 and 8.0% in 2020. However, a study conducted in 2005 reported that FMGs comprised 23–28% of the medical workforce in Canada, Australia, the United Kingdom, and the United States of America.[6] Most of these developed nations depend on foreign-trained doctors to fill postgraduate residency positions as the number of locally-trained doctors does not meet their needs. In addition, the reliance of developed nations on FMGs shows that these (developed) nations have the political will and resources to expend on making up for the inadequacy of the nations’ locally trained doctors. In contrast, many African countries including Nigeria have in recent times experienced a consistent and enormous exodus of their health human resources.[7] Brain drain is currently a significant challenge of human resources for health in Nigeria. Nigeria has about 70,000 registered medical doctors, half of whom currently practice outside its shores.[5] While there is gross inadequacy in the doctor–patient ratios in Nigeria, the nation’s political will and capacity to retain its locally trained doctors and attract foreign-trained doctors is very low. It is pertinent to note that the immigration of foreign-trained medical and dental graduates into Nigeria may not always be due to economic advantage, but likely to returnees seeking Nigerian medical and dental council certification. The reason for the scarcity of foreign-trained ECDs in Nigeria could include the unattractiveness of the Nigerian health-care sector to foreign-trained doctors or the difficulty they encounter in getting licensed by the Medical and Dental Council of Nigeria or even gaining admission into residency training. It may also be that it is mainly home-trained doctors who could cope with the rigors associated with training in Nigerian health-care institutions. Our data suggest that Nigeria relies predominantly on home-trained doctors to fill up its meager physician–patient ratio of about 1:6000 in Nigeria.[5] Both CHARTING (I and II) datasets demonstrated that home-trained ECDs appear to remain a significant constituent of the ECDs workforce (96.7% and 92.0% in 2019 and 2020, respectively) in Nigeria. Thus, foreign-trained ECDs may not be a significant restocking opportunity for the Nigerian health system.[8] Unfortunately, while Nigeria has difficulty attracting ECDs, an increasing number of ECDs are leaving the country for greener pastures. Nigeria expends resources on its medical schools and residency training in the country for training of medical personnel, who conversely migrate to other nations without a corresponding influx from other countries. Another interesting angle to these CHARTING (I and II) datasets is that while Nigeria exports to Western Europe, and North America, she (Nigeria) imports doctors from other African, South-East Asian, and Caribbean countries. However, the importation of ECDs into Nigeria is very low at 3.3% in 2019 and 8% in 2020: this highlights the weak potential of importing ECDs as a source of meeting Nigeria’s health system demand. This deficit in key health-care personnel for the ailing health-care system of Nigeria should be a major source of concern for the government and people. Therefore, efforts should be made to make training and service enticing to both locally and foreign-trained doctors to improve the health and socioeconomic indices in the country. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".