Factors Related to Recruitment and Retention of Doctors on Remote Islands: A Systematic Review
Bibliographic record
Abstract
The doctor shortage is a general healthcare problem on many remote islands, which differ geographically from the mainland. Revealing the factors for recruitment and retention of doctors on remote islands may help develop countermeasures. We systematically investigated the factors related to recruitment and retention of doctors specifically on remote islands. The literature available from PubMed, CENTRAL, and Embase up to May 2024 was reviewed. Eligible studies were original articles with cohort, case-control, cross-sectional, and interventional designs that focused on recruitment and retention of doctors on remote islands. For this study, the "island" was defined as an area and region surrounded by water and separated from the "mainland". The risk of bias was evaluated using the Newcastle-Ottawa Quality Rating Scale. We identified nine eligible original articles, including six cohort studies and three cross-sectional studies on recruitment and retention of doctors reported in Hawaii, Tasmania, and Maluku. In four studies on recruitment, the identified factors were island background, medical education on islands, and quality of life on islands. In six studies on retention, the identified factors were island background, medical education on islands, quality of life on islands, opportunities for professional training, family-related factors, working conditions, and financial incentives. These findings would be useful for policymakers and healthcare planners to secure doctors on remote islands. Further studies are warranted.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".