The learning triad: a sociocultural analysis of internationally educated health professionals’ career pathways in Canada
Bibliographic record
Abstract
While highly sought after as immigrants, internationally educated health professionals (IEHPs), especially those from non-Western countries, face significant challenges in accessing employment in the Canadian healthcare sector. In this context, this paper examines the strategies that IEHPs mobilise to enter the healthcare professions in Canada. Drawing on a life history style study with 22 IEHPs, this paper shows that the pathways of IEHPs into the healthcare sector are paved by a triad of navigational work, professional learning, and identity work. Navigational work refers to the informational sourcing and learning that immigrants undertake to navigate the institutional complex of immigration, education, and professional regulation. Professional learning involves immigrants acquiring professional membership in Canada and expanding their professional knowledge and practices. Identity work refers to the challenges and struggles immigrants experience, individually and sometimes collectively, as they reposition themselves within and in relation to their respective professions. The study highlights the features of communities, activities, media, and institutional practices that shape immigrants’ learning on their pathways to health professions. Grounded in sociocultural learning theories, this paper contributes to the understanding of immigrant learning as a socially organised practice and provides insights into more effective support for immigrants’ integration into regulated professions.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".