Japanese public health nurses’ culturally sensitive disaster nursing for small island communities
Bibliographic record
Abstract
Objective: To clarify the tacit knowledge of Japanese public-health nurses who administer culturally sensitive disaster nursing for small island communities. Design: Qualitative and inductive study. Sample: Eleven public-health nurses who provided disaster aid on one of six affected islands. Measurements: Semi-structured interviews, with qualitative analysis of data. Nursing actions that were based on consideration for islanders’ culture were categorized in terms of similarity. Results: Categories of culturally sensitive disaster nursing were identified for each disaster phase of the recovery process. These included confirming islanders’ safety and using existing interpersonal bonds to notify others (acute phase); assisting shelter management by facilitating the application of local rules and bonds (semi-acute phase); compensating for weakened neighbour-based relationships through public services (mid-term phase); and supporting the completion of necessary procedures by utilizing/adjusting islanders’ existing relationships with local government personnel (long-term phase). Cultural elements included interpersonal bonds and relationship, which emerged across phases. Conclusion: Public-health nurses should utilize culture not only to comfort islanders, but also to strengthen their sense of coherence and resilience as islander. They should also remember the nursing principle of compensating for a lack of self-care. To provide effective aid, the changes in cultural influences with recovery phases should be considered.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".