Care‐giving as a <scp>C</scp> anadian‐ <scp>V</scp> ietnamese tradition: ‘It's like eating, you just do it’
Bibliographic record
Abstract
The objective of this study was to examine how Vietnamese family caregivers (FCGs) perceive, manage and experience end-of-life care-giving for seriously ill family members. Using an instrumental case study design, this longitudinal qualitative research employed the use of cultural brokers/language interpreters to help ensure that the research was conducted in a culturally-appropriate manner. Participants (n = 18) discussed their experiences of care-giving within the context of a traditional cultural framework, which was found to influence their motivations and approaches to care-giving, as well as their propensities towards the use of various supports and services. The study was carried out in southern Ontario, Canada, and participants were providing home-based care-giving in the community. Data were collected throughout 2010 and 2011. The ways in which care-giving was perceived and expressed are reflected in three themes: (i) Natural: identity and care work; (ii) Intentional: whole-person care; and (iii) Intensive: standards, struggle and the context of care. This research confirms the need for culturally-appropriate services and supports while illustrating that Vietnamese FCGs not only value, but are also likely to use healthcare and social services if they are language-accessible, built on trust and demonstrate respect for their values as individuals, regardless of culture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".