Health Resilience in Arabic-speaking Adult Refugees With Type 2 Diabetes: A Grounded Theory Study During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVES: This qualitative study aimed to describe the lived experiences of Arabic-speaking refugees in managing their type 2 diabetes mellitus (T2DM) while resettling during the COVID-19 pandemic, and to generate a grounded theory of how resilience is used to facilitate living well while facing multiple health stressors. METHODS: A grounded theory approach was used to conceptualize the dynamic process of resilience in living well with diabetes. Five recently resettled adult refugees with T2DM (2 women and 3 men) participated in unstructured individual interviews in Arabic in New Brunswick, Canada, during the pandemic's second wave (October 2020 to March 2021). Interview data were transcribed and analyzed thematically using open, axial, and core category coding followed by member checking. RESULTS: Participants identified self-reliance as the core driver for decision-making, actions, and interpretations in health management while experiencing unplanned instability. The process was found to be facilitated by 4 distinct constructs: knowledge seeking, positive outlook, self-care, and creativity. CONCLUSIONS: The substantive model derived from this study supports a strengths-based approach to clinical assessment and care of refugees with T2DM, notably during disrupted access to primary and preventive services due to forced resettlement and pandemic mitigation measures. More research is needed to increase understanding of how self-reliance can be optimized in resilience-promoting interventions to facilitate diabetes management among populations in posttraumatic circumstances.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".