Challenges and their impact on life satisfaction of overseas trained doctors in host countries
Bibliographic record
Abstract
Overseas Trained Doctors (OTD's) face several challenges whilst going through the registration process to become medical practitioners in host countries.This significantly impacts their life satisfaction.This paper aims to report the challenges OTDs face, the impact of these challenges and the role of social support during the registration process.Ten in-depth, semi-structured interviews and 216 self-administered surveys were conducted with OTDs from Canada, Australia, and New Zealand (CANZ).Thematic analysis of the interviews and statistical analysis of the surveys were completed.It was found that limited House Officer and Observership opportunities (observership is observing other medical practitioners perform their duties without being involved in patient care), lack of adequate, reliable, and clear information; financial constraints; time constraints and lack of government support are the predominant challenges faced by OTDs.The study found several challenges have led to a weak-negative impact on the OTD's life satisfaction whilst social support has a moderate to a positive impact on the OTDs' life satisfaction.These findings provide a theoretical contribution to the OTD literature by adding new challenges that previously remained unexplored.The practical implications of this study include streamlining information and its accessibility to OTDs, equal employment opportunities, supporting OTDs' integration into medical employment, providing financial assistance to OTDs going through the registration process across CANZ and amending current supervision policies of training in Australia.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".