Perception of Dentists regarding Brain Drain in Punjab, Pakistan: A Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Brain drain is a term coined for the migration of highly skilled professionals from the country of their origin to more developed countries. As the trend of moving abroad is on a rise, ever since, it is causing a shortage of dental professionals in our country. The objective of our study is to determine the factors that would compel the dentists of Punjab, Pakistan, to move abroad and how to prevent this migration. METHODOLOGY: It was a cross-sectional survey. Data was collected through a self-constructed and validated questionnaire. The study was done in dental colleges in Punjab, and the duration of the study was one year. The questionnaire was distributed both by hand and via digital sources. RESULTS: There were 155 (33.4%) participants, who were planning to go abroad for higher education, and 274 (59%) had not planned to go out of the country for further studies. Among the study participants, 50 (32.3%) participants were planning to move to the United Kingdom, whereas 23 (14.8%) participants were planning to go to Australia, 44 (28.3%) wanted to move to the US, 10 (6.4%) to Canada and the rest (18%) had plans to move to any other country. CONCLUSION: The majority of dental graduates wish to go abroad for their post-graduation. The dentists of Punjab were willing to serve their country, provided the economic and political situations improved.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".