“I’m always gonna be fish out of water” – A qualitative exploration of Philippine-educated nurses in Ontario, Canada
Bibliographic record
Abstract
Background: Filipinos are amongst the fastest growing visible minority groups in Canada (Laquian & Ma, 2021). Despite the huge number of highly-educated professionals from the Philippines in general, and the great number of Philippine-educated nurses (PENs) working within Canada’s healthcare system, we are not aware of any literature that specifically explored PENs’ experiences with workplace transitions or their employment trajectory and how these have impacted their labour outcomes. Objectives: Using PENs as our case study, our objective is to explore the employment trajectory of these IENs within Ontario’s labour landscape. Methods: Following receipt of research ethics clearance from the University of Windsor Research Ethics Board, we conducted in-depth interviews with 15 PENs. All interviews were audio-recorded and transcribed verbatim. Data was managed using ATLAS.ti 23, and analyzed using Braun and Clarke’s (2019) framework for thematic analysis. Results: Four themes were identified from participant interviews: 1) adversity and workplace challenges; 2) preparing for work and bridging education; 3) microaggression, stereotyping, discrimination, racism; and, 4) planning for the future. Future Applications/Directions: PENs encountered challenges starting their nursing career in Canada. With the ongoing global nursing shortage and the competition amongst high-income countries to recruit internationally educated nurses, there is an urgent need for governments and employers to provide sufficient and appropriate supports to this group of nurses to address the othering experienced by these nurses, to promote their retention in the workplace, and to avoid brain waste. Internationally educated nurses, broadly, should be encouraged in supported in advancing their careers in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".