Religion, support and self‐care experiences: A qualitative descriptive study with Indonesian adults with the chronic disease living in Montreal, Canada
Bibliographic record
Abstract
AIM: To explore and describe the chronic illness self-care experiences of Indonesian immigrants living in Montreal, Canada and to gain a better understanding of how religion and support shaped these experiences. DESIGN: Qualitative description. METHODS: Data were collected from January to March 2020 via semi-structured interviews. Eight men and women participated. The data were thematically analysed. RESULTS: Major themes identified were (1) religion, (2) being helpful to others, (3) family support, (4) transnational family support, (5) community support and (6) being in Indonesia versus Canada. Religion and faith were sources of motivation for self-care and provided guidance and strength to heal and accept the illness, mainly through the practice of prayer. 'Being helpful to others' (collectivism), including aiding others to avoid getting sick or giving 'health tips', and also just generally taking care of family also contributed to overall well-being. Spouses were the main source of assistance with disease monitoring and management and health maintenance, whereas support from the Indonesian community was minimal and mostly consisted of informational and social support. Transnational relationships with family members in Indonesia, however, provided an additional means for obtaining emotional support, advice and access to traditional medicines. Overall, there was little expectation that family or the community offer or provide support with self-care. These low expectations may partially be explained by the different cultural and social contexts in Canada compared with Indonesia. CONCLUSION: Religious, cultural, social and family factors may be carried over from the home country and/or may be altered post-migration, and this may impact how Indonesian immigrants with chronic illness engage in self-care. IMPACT: Cultural factors (collectivism, traditional medicines), religious beliefs and support networks, both locally and transnationally should be assessed and considered during care to better support and promote self-care among immigrants living with chronic diseases. PATIENT OR PUBLIC CONTRIBUTION: Two Indonesian community organizations facilitated recruitment and data collection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".