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Enregistrement W4323345375 · doi:10.4103/njm.njm_98_22

Foreign-trained early-career doctors and dynamics in Nigeria: Findings from the charting studies

2022· article· en· W4323345375 sur OpenAlexaboutno aff
UgoUwadiako Enebeli, Aderopo Adelola, Oladimeji Adebayo, OlayinkaStephen Ilesanmi, ShehuSalihu Umar, DareGodiya Ishaya

Notice bibliographique

RevueNigerian Journal of Medicine · 2022
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Workforce Issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDynamics (music)PsychologyMedical educationMedicinePedagogy

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,667
Score d'incertitude au seuil0,954

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,067
Tête enseignante GPT0,402
Écart entre enseignants0,335 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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