A partnership in communication: a qualitative study on the experiences of Punjabi clients as users of interpreters
Bibliographic record
Abstract
Health care practitioners are constantly challenged when providing services to culturally diverse communities. Communication barriers such as differences in culture and language, lack of education about health care services, cultural insensitivity on the part of the practitioner and system ineffectiveness prevent access to health care services by Punjabis and visible ethnic communities as a whole. In this practicum thesis, I present my personal and professional insights, research and client perspectives on how interpreters can help culturally diverse communities to access health care services. I discuss my qualitative study where participant observations and semi-structured interviews were conducted with Punjabi clients to explore their experiences of using interpreters. The focus is specifically on Punjabi clients. Since South Asians (which includes Punjabis) are the fifth largest single ethnic community in Canada and the third largest in British Columbia, it is important to understand their experiences. The findings suggest that many Punjabi clients are satisfied with their use of an interpreter. They appreciate having an interpreter take the time to explain different cultural beliefs and difficult concepts, translating from one language to another, and catching missed material This study has implications for health care practitioners in working with interpreters to develop a health care service delivery system that is accountable to all culturally diverse communities.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".