Human Resource Shortages Caused by the Emigration of Moroccan Health Professionals to Canada: A Focus on Sdgs, With Particular Emphasis on SDG 3 To Ensure Healthy Living and Well-Being at All Ages
Bibliographic record
Abstract
Aim: The aim of this study is to analyse and determine the key factors influencing the decision of Moroccan nurses, health technicians and midwives to immigrate to Canada, despite the shortage of human resources suffered by the Moroccan health ministry, which could undermine the achievement of the 3rd Sustainable Development Goal, aimed at guaranteeing a healthy life and promoting well-being for all at all ages for sustainable societies. Materials and Methods: This research was based on an exploratory study carried out over 2-year period, between janvier 2022 and janvier 2024, targeting nurses health technicians, and midwives who had begun their administrative procedures with the health department to immigrate to Canada. Results: The results showed the existence of several factors that influenced the decision of these health professionals. Based on the literature, interviewees' responses were categorized into four key factors, namely, economic, occupation, organizational, and other factors. In addition, this study allowed us to demonstrate what are the predominant factors that affect the decision to emigrate to Canada health professionals, as an example of working conditions considered unfavorable, low remuneration, and especially the opportunity to practice in Canada that attracts these professionals. The results of this study illustrated several factors that influence the decision to leave health professionals to practice in Canada. Discussion: This is the first study to examine the factors influencing the departure of Moroccan healthcare professionals to practice in Canada. its results will provide a roadmap for decision-makers to implement human resources management tools and mechanisms to retain healthcare professionals, with the ultimate aim of limiting this hemorrhagia that hinders the proper functioning of the care facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".