Factors influencing the intention of doctors to emigrate: a cross-sectional study of Ghanaian doctors
Bibliographic record
Abstract
BACKGROUND: The migration of healthcare professionals from developing countries to more developed nations poses a significant challenge to healthcare systems in low- and middle-income countries. This study aimed to determine the proportion of doctors in Ghana who intend to migrate abroad and to identify the sociodemographic and "pull and push" factors that influence their intention. METHODOLOGY: A cross-sectional survey was conducted among doctors in Ghana between March 1, 2024, and March 15, 2024, via an online-based semi-structured questionnaire. Doctors working in Ghana, regardless of nationality, were included. Descriptive statistics and logistic regression analyses were conducted to identify factors associated with the intention to emigrate. Statistical significance was set at a p-value of < 0.05. RESULTS: Almost all the doctors who responded to the questionnaire consented to participate (99.4%, 641/645). More than half (53.8%, n = 345) of the respondents were medical officers. Most respondents intended to migrate to practice abroad (71.8%, n = 460). The United States (59.7%), the United Kingdom (39.1%), and Canada (34.8%) were the most preferred destinations. After adjusting for covariates, young doctors between 20-29 years [(Adjusted Odd Ratios) AOR = 2.69, 95% CI = 1.13-6.39)], male doctors (AOR = 1.53, 95% CI = 1.04-2.25), doctors in lower professional ranks, and doctors in the field of diagnostics (AOR = 5.70, 95% CI = 1.16 - 28.03) had significantly higher odds of intending to migrate. In descending order of magnitude, the respondents strongly agreed that better remuneration (1.22 ± 0.63), better quality of life (1.22 ± 0.67), better working conditions (1.26 ± 0.69), and better postgraduate training (1.41 ± 0.80) were pull factors. The push factors were economic challenges (1.17 ± 0.49), a lack of a conducive working environment (1.56 ± 0.86), slow career progression (1.95 ± 1.07), excessive workload (2.07 ± 0.12), personal circumstances (2.26 ± 1.19), and poor postgraduate training (2.48 ± 1.22). CONCLUSION: A substantial proportion of doctors in Ghana are considering emigration, driven by a combination of attractive opportunities abroad and challenging conditions in Ghana. Addressing these issues through improved remuneration, better working environments, and enhanced career development and training opportunities is crucial to retaining healthcare professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".