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Record W4307641558 · doi:10.1186/s12960-022-00772-7

Prevalence and pattern of migration intention of doctors undergoing training programmes in public tertiary hospitals in Ekiti State, Nigeria

2022· article· en· W4307641558 on OpenAlexaboutno aff
Adebowale Femi Akinwumi, Oluremi Olayinka Solomon, Paul Oladapo Ajayi, Taiwo Samuel Ogunleye, Oladipupo Adekunle Ilesanmi

Bibliographic record

VenueHuman Resources for Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHealth administrationHealth services researchSocial policyPublic healthState (computer science)MedicineFamily medicineNursingMedical educationPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Background Emigration of Nigerian doctors, including those undergoing training, to the developed countries in Europe and Americas has reached an alarming rate. Objective This study aimed at assessing the prevalence, pattern, and determinants of migration intention among doctors undergoing residency and internship training programmes in the public tertiary hospitals in Ekiti state, Nigeria. Methods This was a cross-sectional study using a quantitative data collected from 182 doctors undergoing residency and internship training at the two tertiary hospitals. An adapted semi-structured questionnaire was used to collect information on migration intention among the eligible respondents. Univariate, bivariate and multivariate data analyses were done. The level of significance was determined at p -value < 0.05. Results Majority (53.9%) of doctors undergoing training were between 30–39 years, and the mean age was 33.2 ± 5.7 years, male respondents were 68.1%, and 53.8% of the respondents were married. The proportion of doctors undergoing training who had the intention to migrate abroad to practice was 74.2%. A higher proportion of the internship trainees, 79.5%, intended to migrate abroad to practice while the proportion among the resident doctors, was 70.6%. Among the respondents who intended to migrate abroad to practice, 85(63%) intend to migrate abroad within the next 2 years, while the preferred countries of destination were the United Kingdom 65(48.2%), Canada 29 (21.5%), Australia 20 (14.8%) and the United States 18(13.3%). Seventy percent of respondents who intend to migrate abroad had started working on implementation of their intention to migrate abroad. The majority of the junior resident doctors, 56(72.7%), intend to migrate abroad compared with the senior resident doctors, 21(27.3%), ( χ 2 = 14.039; p < 0.001). The determinants of migration intention are the stage of residency training and level of job satisfaction. Conclusion There is a high prevalence of migration intention among the doctors undergoing training in the public tertiary hospitals in Ekiti State, Nigeria, with the majority already working on their plans to migrate abroad. Doctors undergoing training who are satisfied with their job and those who are in the senior stage of residency training programme are less inclined to migrate abroad. Recommendations The hospital management in the tertiary hospitals should develop retention strategies for human resources for health, especially doctors undergoing training in their establishment, to avert the possible problems of dearth of specialists in the tertiary health facilities. Also, necessary support should be provided for the residency training programme in the tertiary health institutions to make transition from junior to senior residency stage less strenuous.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.060
GPT teacher head0.403
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations34
Published2022
Admission routes1
Has abstractyes

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