Exploring the role of internationally educated nurses in disaster nursing: The HEAL model
Bibliographic record
Abstract
Natural disasters continue to pose serious threats, causing significant damage and loss of life. The involvement of Filipino internationally educated nurses (IENs) has become increasingly important for effectively managing and responding to such crises. Although the healthcare industry plays a central role in disaster management, the specific contributions of IENs in this context have not been thoroughly examined. Additionally, the environmental implications of IEN recruitment point to the significance of their engagement in climate mitigation. Leveraging the HEAL Model—Help, Educate, Act, and Lead—IENs play crucial roles in training officials, educating peers, leading environmental initiatives, and advocating for policy changes. Their collaboration with volunteers during disaster situations underscores their effectiveness. The IENs, in collaboration with volunteers, bring substantial contributions to disaster nursing and are showcased through the HEAL Model, signifying their potential for enhancing disaster response and climate action endeavors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".