Mentorship: A Powerful Tool for IPG Success
Bibliographic record
Abstract
Because Canada espouses principles of diversity and multiculturalism, many international pharmacy graduates (IPGs) immigrate to Canada expecting to find employment using skills for which they trained in their home country. Upon arrival, they often face challenges in credential recognition and licensure. Barriers include systemic discrimination, socio-psychological isolation, the precipitous decline in social status, and financial challenges of navigating the steps that bridge the training received in their home countries to the scopes of practice in Canada. The problem of practice (PoP) explored in this organizational improvement plan (OIP) focuses on the lack of opportunity that IPGs have to access clinical workplace settings prior to being assessed for entry to practice competencies. Health Alliance is an organization that works in the regulatory space for internationally educated healthcare professionals, and that provides a service that facilitates the IPG path to licensure in Canada. This OIP proposes housing a mentorship program at Health Alliance, to specifically address the experiential learning, and knowledge and skill gaps that have been identified as barriers to success for international pharmacy graduates pursuing licensure, and ultimately, gainful employment as pharmacists in Canada. This OIP examines the PoP through the lenses of sense of community theory and critical race theory to explore how the lived experiences of diverse internationally educated skilled immigrants are impacted by the process of seeking credential recognition and licensure. Change at the leadership, cultural and operational levels will be facilitated through Kotter’s eight stage change model and will be evaluated using an empowerment evaluation approach.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".