Examining the impact of COVID-19 pandemic on international migrants' repatriation intention using structural equation modeling
Bibliographic record
Abstract
Purpose COVID-19 pandemic has shattered the economic systems all around the world while creating numerous problems which were faced by all, especially international migrants. The present study offers a qualitative and quantitative perspective on the distress of international migrants and their repatriation intention during the pandemic period. Design/methodology/approach In-depth semi-structured interviews of 30 respondents belonging to five host nations, Australia, the USA, the UK, New Zealand and Canada, revealed diverse issues. Based on qualitative study findings and past literature, 22 purposeful statements about six constructs – financial issues, social issues, mobility constraints, psychological problems, healthcare issues, and repatriation intentions – were developed. These statements were measured on a seven-point Likert scale and shared online with international migrants from India residing in the host nations. Data collected from 496 international migrants from October 2020 to July 2021 were used to analyze the influence of various determinants on the repatriation intentions by partial least square-structural equation modeling using SmartPLS software. Findings The analysis results revealed that the role of financial, social, mobility, psychological and healthcare issues was significant in strengthening the repatriation intentions of the migrants. There is a need to create job opportunities, retrain laid-off workers and formulate migrant inclusive policies. Originality/value Although some studies have highlighted a few problems faced by international migrants, their impact on repatriation intentions has not been studied yet. The present study fills this gap and analyzes the repatriation intention of international migrants in light of different problems they faced during the pandemic. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2022-0233 .
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".