Participatory community Action Research process addressing employment integration of internationally trained professionals (ITPs) in Canada
Bibliographic record
Abstract
This paper describes the use of a community action research process (CARP) to understand the lived experiences of internationally trained professionals’ (ITPs) unemployment and underemployment in a Community-Based Participatory Action Research (CBPAR) project in Edmonton, Canada. Through a mixed methods design, members of six ethno-cultural communities discussed their challenges, opportunities, and prospects for labour market integration; particularly within the context of an economic uncertainty and downturn as they transition and settle in the western Canadian city of Edmonton. The CARP was utilized through several stages involving a robust recruitment and data collection strategy to facilitate community dialogue about barriers and facilitators impacting ITPs’ employment integration, and engage community members in providing solutions to support current ITPs. The CARP stimulated stakeholders to become more cognizant of the contextual issues impacting ITPs, while taking active roles. Key features of the evaluation process focused on the following: communication patterns, engagement process, applicability of recruitment strategies, effectiveness of mobilization strategies and prospects for community engagement. The CARP proved to be an effective strategy for engagement and facilitating inter-sectoral collaborations across a variety of key stakeholders.
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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 | 0.048 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".