Happy in The Homeland? Satisfaction with Life and its Correlation with Flourishing and Affect Balance in Foreign-trained physicians who have Repatriated to Pakistan
Bibliographic record
Abstract
Introduction International Medical Graduates add up to nearly one-third (23-28%) of the total physician workforce in the US, the UK, Australia, and Canada, of which around 40%-75% are from low-income countries. Pakistan is one of the three leading source countries following India and the Philippines. As per 2002 statistics by the Bureau of Emigration and Foreign Employment, only 10-15% of emigrated Pakistani physicians repatriate. No official data on the exact number of repatriated Pakistani physicians are available. This is the first original study that assessed satisfaction with life in physicians repatriating to a lower-middle-income country. Objectives To assess satisfaction with life (SWL) and its correlation with psychological well-being in foreign-trained, repatriated Pakistani physicians. Methods We conducted this cross-sectional study from April’22 to Nov’23, through purposive sampling among foreign-trained Pakistani physicians who repatriated at least three months before participating. We used the Scale of Positive and Negative Emotions (SPANE), Flourishing Scale, and Satisfaction with Life (SWL) scale. After transforming data to normality in SPSS 25 through the Distribution Fraction method, the independent sample t-test, and one-way ANOVA were applied. We assessed the correlation between affect balance, flourishing, and SWL through Pearson’s correlation and ascertained the predictors of SWL through binary logistic regression (α=0.05). Results Of 109 respondents, the majority (70.6%) were males, from Punjab (83.5%), trained in USA/Canada (68.8%), and from the private sector (69.7%), with a Mean±SD age of 47.31±7.9. The total Mean ± SD SWL scores were 27.48± 5.03. 99 (90.8%) were satisfied with life while only 9.2% weren’t. The currently married respondents (27.8±4.9 vs 24.6±5.4, p=0.04) while those from Sindh and KPK provinces had lower scores. We found a positive moderate correlation between SWL and flourishing (r=0.488), positive emotions (r=0.391), and affect balance (r=0.327) all at p=<0.001. We found good fitness of the final model with SWL as the outcome variable and Flourishing and Overall Affect Balance as predictors: Omnibus Tests of Model Coefficients (p=0.001) and Hosmer and Lemeshow tests (p=0.322). Only flourishing predicted SWL and with higher perceived flourishing, there were 1.3x higher odds of SWL. Conclusions This is the pioneer study to have addressed SWL and its correlation with psychological well-being in repatriated physicians, who using their skill sets and expertise, may help strengthen the healthcare system of the lower-middle income countries of origin, hence it’s imperative to identify factors linked to their psychological well-being. We recommend further research on this particularly qualitative exploration. Disclosure of Interest None Declared
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".