Time Orientation Needs To Be Considered When Engaging In Cardiovasculr Risk Counseling With South Asians
Bibliographic record
Abstract
Background: Healthcare providers tend to have a future orientation when discussing disease risk with patients. It is unclear whether this approach is effective with south Asians relative to Whites residing in Canada. Methods: This was an exploratory study in which south Asian (100) and White (100) people were surveyed using the Zimbardo Time Perspective Inventory. Mean subscale scores and their ranking were compared between ethnic, ethnic and sex, as well as ethnic and age groups. Results: South Asians had higher present-fatalistic and future time orientation scores than Whites. South Asians who had immigrated >5 years ago (and who were older), had higher present-fatalistic, but not future orientation scores, than those who had immigrated more recently or who were Canadian-born (and were younger). Women (particularly south Asian women) had higher past-negative and present-fatalistic scores than men. South Asians >65 years had higher past-negative, present-hedonistic, and present-fatalistic than Whites. Past-positive was differentially ranked highest by the greatest proportion of both south Asians (39%) and Whites (66%). Conclusions: Present-fatalistic orientations are associated with certain subgroups of the south Asians studied (those who had immigrated to Canada >5 years previously, were older, or were women). The findings question the appropriateness of delivering future-oriented health promotion interventions to south Asians, who may be more fatalistic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".