Career mobility among immigrant registered nurses in Canada: Experiences of Caribbean women
Bibliographic record
Abstract
Since the late 1950s, the Canadian nursing workforce especially during periods of nursing shortage has added to its numbers through immigration. Changes in immigration laws since the 1960s have opened doors for increased numbers of immigrants from Asia, Africa, and the Caribbean coming to work in Canada. This qualitative research study investigated the experiences of immigrant women from the Caribbean who are registered nurses (RNs) in Canada. There has been no previously published studies that documented experiences of immigrant women of colour related to career mobility in nursing. A convenience sample of 14 women from the Caribbean, who migrated to Canada between the 1960s and the early 1990s, was recruited for the study. Data were gathered through in-depth interviews, using a semi-structured interview guide. Demographic data were also obtained using a questionnaire that elicited written responses. The analysis of data demonstrated that participants encountered significant barriers in navigating their careers as RNs. These barriers appeared to be related to systemic practices that influenced the regulation of nursing, as well as relationships in work environments. In spite of their experiences in encountering many barriers, participants had developed individual strategies of resistance, and moved forward in their careers in nursing. The study proposes antiracism strategies to create equitable status and rewards for immigrant and minority groups in nursing, as well as for the profession as a whole. The conceptual framework for the research drew from a synthesis of concepts from several bodies of literature; the most significant included critical feminist theories and antiracism discursive frameworks. Foucault's methods contributed to an analysis of the links between power, knowledge and resistance. Among the themes that contributed to an integrated conceptual framework for this research were history, identity, representation, marginalization, power, knowledge, agency and resistance. The research questions were: What factors create barriers or act as facilitators to career mobility among immigrant women of color who are RNs in Canada? How were their lived experiences as RNs mediated through race, gender and class?
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".