Minimising skills wastage: Maximising the health of skilled migrant groups
Bibliographic record
Abstract
<p>Skilled migration is a key element in Australias strategy to address major humancapital issues and imperatives, however underutilisation and atrophy of professionalmigrant skills remains a critical problem. The proposed project aimed to identify barriersand innovative strategies for ensuring utilisation of professional migrants skills andto investigate the links between workforce participation and health. The researchused a sequential exploratory mixed methods design comprising both qualitative andquantitative data collection methods across three research phases.</p><p><strong>Phase 1: </strong>Examined literature on skilled migration in Australia, Canada and NewZealand. An analysis of Census data and data from the Department of Immigration andBorder Protection was also undertaken.</p><p><strong>Phase 2: </strong>Thirteen semi-structured interviews were conducted with key stakeholdersworking in government, policy, industry representation and community based services.The findings from phase 2 of the study informed the development of the quantitativeonline survey.</p><p><strong>Phase 3:</strong>508 skilled migrants responded to an online survey and 14 were theninterviewed.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".