Surviving the employment gap: a cross‐sectional survey of internationally educated nurses
Bibliographic record
Abstract
AIM: To examine the extent to which the type of financial assistance (personal resources, social programmes and earnings) and source country influence the length of time for internationally educated nurses to secure employment as regulated nurses in Canada. BACKGROUND: Internationally educated nurses must professionally recertify in order to work as regulated nurses in Canada. For many, it can be a lengthy, cumbersome and costly process that delays employment, while others recertify and secure employment quickly. Financial assistance in the form of personal resources, or from social programmes or earnings from working could contribute to the length of time to recertify. When internationally educated nurses cannot readily recertify, they turn to survival jobs where they can remain and never practice their profession in Canada or leave the country to work in jurisdictions where it easier to obtain professional credentials. METHODS: Data were collected via cross-sectional survey of internationally educated nurses (n = 1186) who were immigrants, permanent residents and employed as regulated nurses. Multiple linear regression was employed to examine the influence of the type of financial assistance (personal resources, social programmes and earnings) and source country on time to regulated nurse employment. RESULTS: Regression model explained 9.3% of variance in time to regulated nurse employment. Three predictors were statistically significant: source country, social programmes and earnings. Personal resources was not a significant predictor. CONCLUSION: Financial assistance helps internationally educated nurses survive the regulated nurse employment gap. The type of financial assistance and source country influences the length of time to regulated nurse employment. IMPLICATION FOR NURSING AND SOCIAL POLICY: Provides initial evidence to support the development of policies, and educational and social programmes to assist internationally educated nurses with financially surviving the gap in regulated nurse employment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".