Exploring the Canadian Market for Indian Health Workers
Bibliographic record
Abstract
The COVID-19 pandemic has re-emphasized the importance of health workers in ensuring healthcare delivery as well as preparedness for sudden health shocks. The shortage of healthcare workers aggravated by the pandemic resulted in policies for attracting and retaining foreign health workers in receiving countries. On the other hand, countries such as India, the largest global supplier of health professionals, have launched schemes to boost the supply of health workers, to address domestic and global demand-supply gaps. However, a relatively less explored destination for Indian health workers is Canada, where around 39 per cent of doctors and 25 per cent of nurses were foreign-born in 2016 but a market that does not feature among the top destination countries for Indian health workers. This chapter examines the data on Indian health workers in Canada. It discusses the rising shares of not only Indian health professionals but also other categories of health workers in Canada. The study suggests the need to explore different approaches for collaboration between India and Canada, through enhanced bilateral economic ties that can be beneficial to the health systems of both countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".