Diversifying the health workforce: a mixed methods analysis of an employment integration strategy
Bibliographic record
Abstract
BACKGROUND: Historically, immigration has been a significant population driver in Canada. In October 2020, immigration targets were raised to an unprecedented level to support economic recovery in response to COVID-19. In addition to the economic impact on Canada, the pandemic has created extraordinary challenges for the health sector and heightened the demand for healthcare professionals. It is therefore imperative to accelerate commensurate employment of internationally educated nurses (IENs) to strengthen and sustain the health workforce and provide care for an increasingly diverse population. This study aimed to determine the effectiveness of a project to help job-ready IENs in Ontario, Canada, overcome the hurdle of employment by matching them with healthcare employers that had available nursing positions. METHODS: A mixed methods design was used. Interviews were held with IENs seeking employment in the health sector. Secondary analysis was conducted of a job bank database between September 1 and November 30, 2019 to identify healthcare employers with the highest number of postings. Data obtained from the 2016 Canadian Census were used to create demographic profiles mapping the number and proportion of immigrants living in the communities served by these employers. The project team met with senior executives responsible for hiring and managing nurses for these employers. The executives were given the appropriate community immigrant demographic profile, a manual of strategic practices for hiring and integrating IENs, and the résumés and bios of IENs whose skills and experience matched the jobs posted. RESULTS: In total, 112 IENs were assessed for eligibility and 95 met the inclusion criteria. Twenty-one healthcare employers were identified, and the project team met with 54 senior executives representing these employers. Ninety-five IENs were subsequently matched with an employer. CONCLUSIONS: The project was successful in matching job-ready IENs with healthcare employers and increasing employer awareness of IENs' abilities and competencies, changing demographics, and the benefits of workforce diversity. The targeted activities implemented to support the project goal are applicable to sectors beyond healthcare. Future research should explore the long-term impact of accelerated employment integration of internationally educated professionals and approaches used by other 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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".