Training for impact: the socio-economic impact of a fit for purpose health workforce on communities
Bibliographic record
Abstract
Across the globe, a "fit for purpose" health professional workforce is needed to meet health needs and challenges while capitalizing on existing resources and strengths of communities. However, the socio-economic impact of educating and deploying a fit for purpose health workforce can be challenging to evaluate. In this paper, we provide a brief overview of six promising strategies and interventions that provide context-relevant health professional education within the health system. The strategies focused on in the paper are:1. Distributed community-engaged learning: Education occurs in or near underserved communities using a variety of educational modalities including distance learning. Communities served provide input into and actively participate in the education process.2. Curriculum aligned with health needs: The health and social needs of targeted communities guide education, research and service programmes.3. Fit for purpose workers: Education and career tracks are designed to meet the needs of the communities served. This includes cadres such as community health workers, accelerated medically trained clinicians and extended generalists.4. Gender and social empowerment: Ensuring a diverse workforce that includes women having equal opportunity in education and are supported in their delivery of health services.5. Interprofessional training: Teaching the knowledge, skills and attitudes for working in effective teams across professions.6. South-south and north-south partnerships: Sharing of best practices and resources within and between countries.In sum, the sharing of resources, the development of a diverse and interprofessional workforce, the advancement of primary care and a strong community focus all contribute to a world where transformational education improves community health and maximizes the social and economic return on investment.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".