Cross‐Cultural Relationships Between Nurses and Filipino Canadian Patients
Bibliographic record
Abstract
PURPOSE: To describe culturally embedded values that implicitly guide Filipino Canadian patients' interactions with Canadian nurses and are integral to nurse-patient relationships. DESIGN AND METHODS: A focused ethnography was conducted, with a purposive sample of 23 Filipino-Canadians who received care in Canadian hospitals. Data consisted of interviews, field notes, and diary. FINDINGS: When receiving care, patients delineated hindi ibang tao (one of us) and ibang tao (not one of us) and this determined their preference for who performed personal and private tasks or received information. The urgency of the patients' conditions, the intimacy required for most nursing procedures, and short hospitalizations meant that patients often interacted without progressing through the cultural levels of pakikitungo (formality), pakikibagay (adjustability), and pakikisama (acceptance). Rather, the crisis of being hospitalized forced patients to immediately move toward the cultural levels of pakikipagpalagayang-loob (mutual comfort) or pakikiisa (oneness). Patients' willingness to trust and to share their kapwa-oriented worldview in relating with fellow human beings, and their use of their languages of words, gaze, touch, and food, allowed nurses to become hindi ibang tao (one of us). Caregiving roles and establishing relationships also distinguished that hindi ibang tao (one of us) was to bantay (watch over) the patient, whereas ibang tao (not one of us) was expected to alaga (care for) them (i.e., provide professional care). CONCLUSIONS: Communicating and caring effectively requires understanding of Filipino Canadians' languages of words, gaze, touch, and food and their levels of interaction. Culturally safe nurse-patient relationships can then develop.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".