Bibliographic record
Abstract
The 'Barriers to Access to Care for Ethnic Minority Seniors ' (BACEMS) study in Vancouver, British Columbia, found that immigrant families torn between changing values and the economic realities that accompany immigration cannot always provide optimal care for their elders. Ethnic minority seniors further identified language barriers, immigration status, and limited awareness of the roles of the health authority and of specific service providers as barriers to health care. The configuration and delivery of health services, and health-care providers' limited knowledge of the seniors' needs and confounded these problems. To explore the barriers to access, the BACEMS study relied primarily on focus group data collected from ethnic minority seniors and their families and from health and multicultural service providers. The applicability of the recently developed model of 'candidacy', which emphasises the dynamic, multi-dimensional and contingent character of health-care access to ethnic minority seniors, was assessed. The candidacy framework increased sensitivity to ethnic minority seniors' issues and enabled organisation of the data into manageable conceptual units, which facilitated translation into recommendations for action, and revealed gaps that pose questions for future research. It has the potential to make Canadian research on the topic more co-ordinated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".