Assessing the contribution of immigrants to Canada’s nursing and health care support occupations: a multi-scalar analysis
Bibliographic record
Abstract
BACKGROUND: The World Health Organization adopted the Global Strategy on Human Resources for Health Workforce 2030 in May 2016. It sets specific milestones for improving health workforce planning in member countries, such as developing a health workforce registry by 2020 and ensuring workforce self-sufficiency by halving dependency on foreign-trained health professionals. Canada falls short in achieving these milestones due to the absence of such a registry and a poor understanding of immigrants in the health workforce, particularly nursing and healthcare support occupations. This paper provides a multiscale (Canada, Ontario, and Ontario's Local Health Integration Networks) overview of immigrant participation in nursing and health care support occupations, discusses associated enumeration challenges, and the implications for health workforce planning focusing on immigrants. METHODS: Descriptive data analysis was performed on Canadian Institute for Health Information dataset for 2010 to 2020, and 2016 Canadian Census and other relevant data sources. RESULTS: The distribution of nurses in Canada, Ontario, and Ontario's Local Health Integration Networks reveal a growth in Nurse Practitioners and Registered/Licensed Practical Nurses, and contraction in the share of Registered Nurses. Immigrant entry into the profession was primarily through the practical nurse cadre. Mid-sized communities registered the highest growth in the share of internationally educated nurses. Data also pointed towards the underutilization of immigrants in regulated nursing and health occupations. CONCLUSION: Immigrants comprise an important share of Canada's nursing and health care support workforce. Immigrant pathways for entering nursing occupations are complex and difficult to accurately enumerate. This paper recommends the creation of an integrated health workforce dataset, including information about immigrant health workers, for both effective national workforce planning and for assessing Canada's role in global health workforce distribution and utilization.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".