Source country perceptions, experiences, and recommendations regarding health workforce migration: a case study from the Philippines
Bibliographic record
Abstract
BACKGROUND: The Philippines continues to overproduce nurses for export. Little first-hand evidence exists from leading organisations in the Philippines concerning their experiences and perceptions in relation to Filipino nurse migration. What are their views about health workforce migration? This paper addresses this research gap by providing a source country perspective on Filipino nurse migration to Australia. METHODS: Focus-group interviews were conducted with key informants from nine Filipino organisations in the Philippines by an Australian-Filipino research team. The organisations were purposively selected and contacted in person, by phone, and/or email. Qualitative thematic analysis was performed using a coding framework. RESULTS: Health workforce migration is perceived to have both positive and negative consequences. On the one hand, emigration offers a welcome opportunity for individual Filipino nurses to migrate abroad in order to achieve economic, professional, lifestyle, and social benefits. On the other, as senior and experienced nurses are attracted overseas, this results in the maldistribution of health workers particularly affecting rural health outcomes for people in developing countries. Problems such as 'volunteerism' also emerged in our study. CONCLUSIONS: In the context of the WHO (2010) Code of Practice on the International Recruitment of Health Personnel it is to be hoped that, in the future, government recruiters, managers, and nursing leaders can utilise these insights in designing recruitment, orientation, and support programmes for migrant nurses that are more sensitive to the experience of the Philippines' education and health sectors and their needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".