JOB SEARCH EXPERIENCES OF FEMALE REGISTERD NURSES FROM EAST AFRICA IN TORONTO
Bibliographic record
Abstract
This study examined the challenges female-professional immigrants from East Africa face within the Canadian workforce. The analysis of their experiences helps us understand the employment challenges professional immigrants may face upon settlement in Canada. The main goal of the study was to explore the experiences of East African (Kenyan, Ugandan and Tanzanian) immigrant-female registered nurses in navigating the Canadian labour market. The evidence for the study was collected through interviewing five East African nurses. Although there is research that focuses on labour market experiences of women of colour, few researchers have specifically focused on African immigrant women’s connection with the Canadian labour force. The study particularly focuses on strategies nurses used to cope with the job search barriers encountered, the challenges they faced with the College of Nurses of Ontario with regard to the evaluation of their international-nursing credentials, and their job expectations before and after arriving in Canada. Their experience was examined through gender, race, and place of origin lenses. The study highlights the need for future longitudinal studies exploring East African nurses’ experience with integration to their profession within the Canadian workforce. The analysis of the results emphasizes that the Canadian government in conjunction with the regulatory bodies need to be more transparent in relation to internationally trained nurses so that they do not feel they are being wasted in Canada. This, in turn, will address the existing barriers and consequential negative impacts such as health conditions, tensions, and discrepancies outlined within the study, as well as encourage changes to Canadian immigration practices and policies
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".