世界経済・社会統計 = World development indicators

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

Sustainable Development Goals

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The World Bank Group

Editor – I am moved by Professor Allan's elegy to bygone NHS virtues of ‘calm caring and gentle pace of clinical life… and all the time in the world to deliver compassionate care' ( Clin Med October 2009 p 407). One's immediate instinct would be to say ‘Ah, but times have changed' – only

Quality of care.

Validation of the Decision model of the Burden of Hearing loss Across the Lifespan (DeciBHAL) in Chile, India, and Nigeria

Objective Tracking global health funding is a crucial but time consuming and labor-intensive process. This study aimed to develop a framework to automate the tracking of global health spending using natural language processing (NLP) and machine learning (ML) algorithms. We used the global common goods for health (CGH) categories developed by Schäferhoff et al. to design and evaluate ML models. Methods We used data curated by Schäferhoff et al., which tracked the official development assistance (ODA) disbursements to global CGH for 2013, 2015, and 2017, for training and validating the ML models. To process raw text, we implemented different NLP techniques, such as removing stop words, lemmatization, and creation of synthetic text, to balance the dataset. We used four supervised learning ML algorithms—random forest (RF), XGBOOST, support vector machine (SVM), and multinomial naïve Bayes (MNB) (see Glossary)—to train and test the pre-coded dataset, and applied the best model on dataset that hasn't been manually coded to predict the financing for CGH in 2019. Results After we trained the machine on the training dataset (n = 10,534), the weighted average F1-scores (a measure of a ML model's performance) on the testing dataset (n = 2,634) ranked 0.79–0.83 among four models, and the RF model had the best performance (F1-score = 0.83). The predicted total donor support for CGH projects by the RF model was $2.24 billion across 3 years, which was very close to the finding of $2.25 billion derived from coding and classification by humans. By applying the trained RF model on the 2019 dataset, we predicted that the total funding for global CGH was about $2.7 billion for 730 CGH projects. Conclusion We have demonstrated that NLP and ML can be a feasible and efficient way to classify health projects into different global CGH categories, and thus track health funding for CGH routinely using data from publicly available databases.

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Tracking financing for global common goods for health: A machine learning approach using natural language processing techniques

Converting HIV programmes into chronic-care platforms

Th is article describes the integrated, mixed methods (MM) design used to evaluate the Saving Lives at Birth (SL@B) program. SL@B is a multi-stakeholder, donor-supported global health initiative to tackle maternal and neonatal mortality via innovation. Since SL@B’s launch in 2011, the program has supported 116 innovationsthrough 147 awards around the globe. The evaluation for this large and complex program included a largely retrospective MM design aligned with principles of evaluating complexity. This paper highlights these MM evaluation strategies and integration dimensions employed to complete the SL@B evaluation that could inform future evaluations of portfolio-level global health programs.

Osondu Ogbuoji

Papers

Validation of the Decision model of the Burden of Hearing loss Across the Lifespan (DeciBHAL) in Chile, India, and Nigeria

Tracking financing for global common goods for health: A machine learning approach using natural language processing techniques

Converting HIV programmes into chronic-care platforms

Integrating Data to Evaluate a Global Health Grand Challenge

The UK's foreign aid cuts: implications for financing health systems globally.