BRAIN DRAIN: INTENTIONS OF HOUSE OFFICERS IN SPECIALIZING FROM PAKISTAN OR ABROAD AND ITS’ VARIOUS INFLUENCING FACTORS AMONGST MEDICAL GRADUATES FROM PESHAWAR, PAKISTAN
Bibliographic record
Abstract
INTRODUCTION: Brain drain is defined as the immigration of highly qualified health professionals from low- income countries to high-income affluent countries in pursuit of a better & safer quality of life. A cross-sectional study was conducted to determine the magnitude of migratory intentions of House Officers of four main tertiary care hospitals based in Peshawar, Pakistan. METHODOLOGY: The research design for this study was a cross sectional study which was carried out in four tertiary care hospitals of Peshawar KPK. The duration of this study was 5 months from March - July 2022. Data was collected through convenient sampling and 195 participants were involved in filling a self-structured questionnaire. RESULTS: The results of this study revealed that out of 195 respondents 104(53.3%) intended to go abroad for specialization while 91(46.67%) opted to stay in Pakistan.The study also showed that out of 195 medical graduates 104(53.3%) opted for abroad in which 32(31.37%) preferred to go United States, 62(60.78%) preferred United Kingdom and 8(7.84%) preferred other countries (Australia and Canada) for post graduate training. 48.6% males and 59.3% females have intentions for going abroad with a p value of 0.138 which is not significant; implying that the there is no difference between males & females in selecting their career choice. CONCLUSION: The findings of our research highlight the reasons behind the immigration of medical graduates. When large number of graduates are going abroad there would be its implications on healthcare and academic policies. This study can help government to make appropriate policies to address brain drain. On basis of data collected if there is economic stability, better training facilities and attractive salary packages are being provided by state of Pakistan then there will a decrease in the number of house officers migrating abroad. KEYWORDS: House officers, Specialization, Brain drain, Residency
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".