Evaluation synthesis analysis can be accelerated through text mining, searching, and highlighting: A case-study on data extraction from 631 UNICEF evaluation reports

Abstract

Background: The United Nations Children's Fund (UNICEF) is the United Nations agency dedicated to promoting and advocating for the protection of children's rights, meeting their basic needs, and expanding their opportunities to reach their full potential. They achieve this by working with governments, communities, and other partners via programmes that safeguard children from violence, provide access to quality education, ensure that children survive and thrive, provide access to water, sanitation and hygiene, and provide life-saving support in emergency contexts. Programmes are evaluated as part of UNICEF Evaluation Policy, and the publicly available reports include a wealth of information on results, recommendations, and lessons learned. Objective: To critically explore UNICEF's impact, a systematic synthesis of evaluations was conducted to provide a summary of UNICEF main achievements and areas where they could improve, as a reflection of key recommendations, lessons learned, enablers, and barriers to achieving their goals and to steer its future direction and strategy. Since the evaluations are extensive, manual analysis was not feasible, so a semi-automated approach was taken. Methods: This paper examines the automation techniques used to try and increase the feasibility of undertaking broad evaluation syntheses analyses. Our semi-automated human-in-the-loop methods supported data extraction of data for 64 outcomes across 631 evaluation reports; each of which comprised hundreds of pages of text. The outcomes are derived from the five goal areas within UNICEF 2022-2025 Strategic Plan. For text pre-processing we implemented PDF-to-text extraction, section parsing, and sentence mining via a neural network. Data extraction was supported by a freely available text-mining workbench, SWIFT-Review. Here, we describe using comprehensive adjacency-search-based queries to rapidly filter reports by outcomes and to highlight relevant sections of text to expedite data extraction. Results: While the methods used were not expected to produce 100% complete results for each outcome, they present useful automation methods for researchers facing otherwise non-feasible evaluation syntheses tasks. We reduced the text volume down to 8% using deep learning (recall 0.93) and rapidly identified relevant evaluations across outcomes with a median precision of 0.6. All code is available and open-source. Conclusions: When the classic approach of systematically extracting information from all outcomes across all texts exceeds available resources, the proposed automation methods can be employed to speed up the process while retaining scientific rigour and reproducibility.