Emotional, relational, technological, and financial dimensions of transnational elder caregiving among Nigerian immigrants in Northern British Columbia
Bibliographic record
Abstract
BACKGROUND/RATIONALE: Migration can alter elder caregiving practices, redistributing responsibilities across geographically dispersed networks. However, systemic barriers such as economic precarity, limited digital access, and immigration constraints often exacerbate the complexities of elder caregiving in transnational contexts. In addition to the common challenges faced by caregivers, such as emotional strain, logistical coordination, and financial demands, transnational caregivers must also navigate the complexities introduced by physical distance from their care recipients. Technology is a critical tool in bridging these gaps, enabling caregivers to provide emotional support, monitor health, and manage caregiving tasks remotely. This study examines how first-generation Nigerian immigrants navigate transnational eldercare, focusing on the interplay between emotional, relational, technological, and financial dynamics, and offers insights into the evolving nature of caregiving in a globalized world. METHODOLOGY AND METHODS: This qualitative study included N = 10 first-generation Nigerian immigrants residing in Northern BC. The integrated frameworks of transnationalism and intersectionality guided the description of how rural and northern geography, immigration status, and class, reflected through education, occupation, and income narratives, shape elder caregiving practices across borders. An inductive reflexive thematic analysis was employed, using narrative interviews and a brief pre-interview survey to contextualize caregiving roles. Data collection included pre-interview surveys to capture demographic and caregiving contexts, and narrative interviews that provided in-depth accounts of participants' caregiving experiences across borders. These methods offered a nuanced exploration of the complexities of transnational elder caregiving. RESULTS: Caregivers expressed guilt, helplessness, and emotional strain, but also resilience through familial support and self-care. Migration redistributed caregiving roles, with local families providing physical care and migrants offering financial support and coordination. Tools like WhatsApp and video calls enabled emotional connection and remote monitoring despite digital limitations. Financial remittances sustained care but introduced economic strain. Family bonds were maintained through virtual collaboration, with caregivers navigating cultural tensions. CONCLUSIONS: This study reveals the adaptability of Nigerian transnational caregivers as they navigate financial, emotional, and logistical responsibilities across borders. While emphasizing resilience, the findings also highlight systemic challenges-including digital inequities and economic pressures-calling on policymakers, healthcare providers, and community organizations to develop culturally informed policies and targeted support that empower caregivers and enhance well-being in transnational settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".