Migration Factors of Clinical Instructors in a University
Bibliographic record
Abstract
The worldwide shortage of nurses, which results from a global undersupplyand high attrition rates, affects developed countries in the West the same way as itaffects developing countries in Asia, Africa and Latin America. The difference liesin the fact that developing countries serve as a readily available source of trainednurses for developed countries in Europe, North America and parts of Oceania.Thus, the ongoing nursing shortage in developing countries is worsened by a lossof thousands of trained nurses every year to emigration. This study identified the migration factors of Clinical Instructors in a university of Cebu City, Philippines.Utilizing 100 clinical instructors as respondents, the study reveals that the majoritywere 25-28 years old; female; single; 0-5 years of work service; with units in amaster’s program; belonged to a nuclear family; has no child; with monthly incomeof Php10,000-Php20,000; has taken foreign nursing examinations; and intendedto migrate to Canada. The top three push factors of migration were low salary, absence of overtime and hazard pays, and limited opportunities for employment.Top three pull factors were higher income, better benefits and compensationpackage, a chance to upgrade nursing skills, and opportunity to travel and learn other cultures.Keywords: Social sciences, low salary, nurse migration, push factors, descriptivecorrelationaldesign, Philippines
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".