The caring attributes of Filipino nurses working abroad utilizing the CASAGRA Transformative Model
Bibliographic record
Abstract
The largest exporter of nurses around the world is the Philippines composing approximately 25% of all overseas nurses worldwide.Roughly 85% of hired Filipino nurses are employed and practicing in more than 50 countries globally.This study focused on exploring the caring attributes of Filipino Nurses working abroad utilizing the CASAGRA Transformative Leadership Model to examine the impact of faculty staff or clinical educators on the caring attributes of Filipino nurses working outside the Philippines and how servant leadership was being manifested in their field of work.Numerous studies were conducted about Filipino Nurses' caring attributes and the role of academe.However, no study has yet been published with regard to the important role of the academe in leading and educating future nursing leaders utilizing the CASAGRA Transformative Model.Through the one-on-one online interviews, data were collected from 16 Registered Nurses, in the Philippines and in their current country of employment, practicing in various units of the hospital currently working in Saudi Arabia, Qatar, Canada, Australia, USA, Germany, and the United Kingdom, who graduated from different universities in the three (3) major island group in the Philippines, Luzon, the Visayas and Mindanao, and different years graduated.Data gathered were then transcribed and analyzed using qualitative content analysis.Life-Changing Care by True Leaders Who Serve was derived from the study through the caring attributes influenced by Clinical Instructors, serving through mentoring, having a harmonious relationship in rendering care, and the desire to serve as a leader.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".