OLDER ADULTS’ IMMIGRANT STATUS AND SELF-REPORTED ABILITY TO NAVIGATE THROUGH THE HEALTHCARE SYSTEM
Bibliographic record
Abstract
Abstract This exploratory study examined the association between older adults’ immigrant status and their self-reported ability to perform each of the 51 self-care behaviors that are needed for them to navigate through the healthcare system. Secondary data analysis was conducted based on a 2018 telephone survey of community-dwelling adults 65 y/o or older, living in a western Canada province (N = 1,000). A previously validated survey tool, Patient Involvement Behaviors in Health Care (e.g., indicating Yes=1 or No=0 regarding their ability to perform each self-care behavior), and a demographic data form (e.g., are you an immigrant? Yes=1 or No=0) were used. Descriptive analyses and chi-square tests for independence (alpha= 0.05) were conducted. Among the 993 adults who indicated their immigrant status, 51 (5.1%) self-declared as immigrants. 32 (62.7%) of the immigrant participants and 457 (48.5%) of the non-immigrant participants resided in the urban areas. 88.2% of these immigrant participants was white, 7.8% was Asian, and 2% was black; 72.5% indicated that English is their first language. Immigrant participants were less likely to report being able to perform 5 self-care behaviors than non-immigrant participants. These 5 behaviors were: bringing someone to help you move around when needed; asking your providers to share your medical record with each other; finding insurance that best matches your needs; changing health insurance coverage as needed; and knowing of any interactions with old and new treatments. Clinicians should co-create approaches with older adult immigrants to improve their self-care capacity (e.g., connecting with relevant peer support networks).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".