A synthesis of recent analyses of human resources for health requirements and labour market dynamics in high-income OECD countries
Bibliographic record
Abstract
BACKGROUND: Recognition of the importance of effective human resources for health (HRH) planning is evident in efforts by the World Health Organization (WHO) and the Global Health Workforce Alliance (GHWA) to facilitate, with partner organizations, the development of a global HRH strategy for the period 2016-2030. As part of efforts to inform the development of this strategy, the aims of this study, the first of a pair, were (a) to conduct a rapid review of recent analyses of HRH requirements and labour market dynamics in high-income countries who are members of the Organisation for Economic Co-operation and Development (OECD) and (b) to identify a methodology to determine future HRH requirements for these countries. METHODS: A systematic search of peer-reviewed literature, targeted website searches, and multi-stage reference mining were conducted. To supplement these efforts, an international Advisory Group provided additional potentially relevant documents. All documents were assessed against predefined inclusion criteria and reviewed using a standardized data extraction tool. RESULTS: In total, 224 documents were included in the review. The HRH supply in the included countries is generally expected to grow, but it is not clear whether that growth will be adequate to meet health care system objectives in the future. Several recurring themes regarding factors of importance in HRH planning were evident across the documents reviewed, such as aging populations and health workforces as well as changes in disease patterns, models of care delivery, scopes of practice, and technologies in health care. However, the most common HRH planning approaches found through the review do not account for most of these factors. CONCLUSIONS: The current evidence base on HRH labour markets in high-income OECD countries, although large and growing, does not provide a clear picture of the expected future HRH situation in these countries. Rather than HRH planning methods and analyses being guided by explicit HRH policy questions, most of the reviewed studies appeared to derive HRH policy questions based on predetermined planning methods. Informed by the findings of this review, a methodology to estimate future HRH requirements for these countries is described.
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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 | 0.029 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.071 | 0.084 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".