Investigating patients with an immigration background in Canada: relationships between individual immigrant attitudes, the doctor-patient relationship, and health outcomes
Bibliographic record
Abstract
BACKGROUND: Increasing immigration in the world today leads to more intercultural interactions. This is a particularly crucial fact in doctor-patient relationships, which often become more complex and suboptimal within an intercultural context. Since acculturation is a particularly important factor in this process, and the doctor-patient relationship is a key component in patient health outcomes, this study investigates the interrelation of individual immigrant acculturation orientations with the quality of the doctor-immigrant patient relationship, the patients' perceived quality of care, and how this relates to immigrant health behaviours and quality of life of the patients. METHODS: 171 immigrant patients of various backgrounds participated in a paper and pencil questionnaire to assess the role of acculturation orientations (AO) on patients' perceived expectations of their doctor, perceived quality of care (PQOC), health behaviours and quality of life. Data were analyzed using ANOVA, regression and correlation procedures with SPSS statistical software. RESULTS: Significant correlations were found between all AOs and measures of the participant feeling connected to the host or home culture, thereby verifying the measure of AO. All four AOs were significantly interrelated directly with the patient's perception of what the doctor expects of him/her, and the patients' quality of life. Patients' perceived expectations of their doctors were significantly related to the patients' PQOC, and PQOC was associated with improved health behaviours (adherence to doctor recommendations, physical activity maintenance self-efficacy). CONCLUSIONS: AO may be an important factor in the doctor-immigrant patient relationship, via a complex process involving the patients' perceptions of doctors' expectations and perceived quality of care. This has important implications, since such an understanding can be used to create interventions for both doctors and immigrant patients to learn about their own AO, how it can relate to the quality of their relationship, and ultimately, the quality of care, health and quality of life of the patient.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".