The Self-care of Older Adult Iranian Immigrants with Diabetes: Implications for Identity and Social Justice
Bibliographic record
Abstract
Self-care has become a central focus in health care policies and practices over the past few decades. Under the neoliberal construction of responsible citizenship, individuals are considered autonomous agents who are expected to be self-reliant and to assume responsibility for their health with the goal of reducing healthcare costs and increasing efficiency. While this might seem to be beneficial to people and healthcare systems, there is a systemic lack of interest in the structural, social, and economic inequities that impact the ability of underprivileged groups to practice self-care. People’s inability to meet the required expectations of self-care may not only have consequences for their health but may also negatively affect their identities and undermine their sense of agency. Little is known about the impact of current self-care directives on the identities of patients with chronic illnesses living in marginal social locations. The purpose of this study was to explore how older adult Iranian immigrants with diabetes understand and negotiate their identities in the context of diabetes self-care demands. This group was chosen because immigrant Iranian older adults may face additional constraints in practicing self-care due to their marginal social location. A feminist ethics framework informed by the works of Hilde Lindemann, Joan Tronto, and Iris Young guided this study. Using a critical qualitative narrative methodology, 15 older adult Iranian immigrants with diabetes living in the Greater Toronto Area were interviewed. Four key findings resulted from this study. First, the moralization and responsibilization of health, with little regard to the structural and relational constraints of people’s lives, can damage people’s identities and sense of agency. Second, the language of control, compliance, and dependency has a negative impact on people’s identities. Third, medicalization and corporatization of healthcare contribute to the marginality of care receivers. Finally, experiencing racialization inside and outside of the healthcare system and biased self-care directives constrain immigrants’ self-care practices. This research suggests that current self-care directives can damage the identities of chronically ill older adults and widen existing social inequalities by legitimizing unjust health disparities. This thesis is a call to move away from individualistic solutions for care by acknowledging our shared interdependence and vulnerabilities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".