Human resources for health in Peru: recent trends (2007–2013) in the labour market for physicians, nurses and midwives
Bibliographic record
Abstract
BACKGROUND: Most analyses of gaps in human resources for health (HRH) do not consider training and the transition of graduates into the labour market. This study aims to explore the labour market for Peru's recent medical, nursing, and midwifery graduates as well as their transition into employment in the Ministry of Health's (MOH) system. METHODS: Data from four different datasets, covering 2007-2013, was used to characterize the patterns of recently trained physicians, nurses, midwives, and postgraduate-trained physicians that enter employment in the MOH system, and scenario analyses were used to describe how this rate of entry needs to adapt in order to fill current HRH shortages. RESULTS: HRH graduates have been increasing from 2007 to 2011, but the proportions that enter employment in the MOH system 2 years later range from 8 to 45% and less than 10% of newly trained medical specialists. Scenario analyses indicate that the gap for physicians and nurses will be met in 2027 and 2024, respectively, while midwives in 2017. However, if the number of HRH graduates entering the MOH system doubles, these gaps could be filled as early as 2020 for physicians and 2019 for nurses. In this latter scenario, the MOH system would still only utilize 56% of newly qualified physicians, 74% of nurses, and 66% of midwives available in the labour market. CONCLUSION: At 2013 training rates, Peru has the number of physicians, nurses, and midwives it needs to address HRH shortages and meet estimated HRH gaps in the national MOH system during the next decade. However, a significant number of newly qualified health professionals do not work for the MOH system within 2 years of graduation. These analyses highlight the importance of building adequate incentive structures to improve the entry and retention of HRH into the public sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".