Reclaiming identities: exploring the influence of simulation on refugee doctors’ workforce integration
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals are a precious resource, however, if they fail to integrate into the workforce, they are likely to relocate. Refugee doctors face workforce integration challenges including differences in language and culture, educational background, reduced confidence, and sense of identity. It has been proposed that simulation programmes may have the power to influence workforce integration. This study aimed to explore how an immersive simulation programme influenced workforce integration for refugee doctors joining a new healthcare system. METHODS: Doctors were referred to a six-day immersive simulation programme by a refugee doctor charity. Following the simulation programme, they were invited to participate in the study. Semi-structured interviews, based on the 'pillars' conceptual model of workforce integration, were undertaken. Data were analysed using template analysis, with the workforce integration conceptual model forming the initial coding template. Themes and sub-themes were modified according to the data, and new codes were constructed. Data were presented as an elaborated pillars model, exploring the relationship between simulation and workforce integration. RESULTS: Fourteen doctors participated. The 'learning pillar' comprised communication, culture, clinical skills and knowledge, healthcare systems and assessment, with a new sub-theme of role expectations. The 'connecting pillar' comprised bonds and bridges, which were strengthened by the simulation programme. The 'being pillar' encompassed the reclaiming of the doctor's identity and the formation of a new social identity as an international medical graduate. Simulation opportunities sometimes provided 'building blocks' for the pillars, but at other times opportunities were missed. There was also an example of the simulation programme threatening one of the integration pillars. CONCLUSIONS: Opportunities provided within simulation programmes may help refugee doctors form social connections and aid learning in a variety of domains. Learning, social connections, and skills application in simulation may help doctors to reclaim their professional identities, and forge new identities as international medical graduates. Fundamentally, simulation experiences allow newcomers to understand what is expected of them. These processes are key to successful workforce integration. The simulation community should be curious about the potential of simulation experiences to influence integration, whilst also considering the possibility of unintentional 'othering' between faculty and participants.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".