Evaluation synthesis analysis can be accelerated through text mining, searching, and highlighting: A case-study on data extraction from 631 UNICEF evaluation reports
Authors: Schmidt Lena , Addis Pauline , Mattellone Erica , O’Keefe Hannah , Nabiyeva Kamilla , Huynh Uyen Kim , Dehingia Nabamallika , Craig Dawn , Campbell Fiona 
Published: August 2024

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 Policy1, and the publicly available reports2include 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;3each 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.

Strengths and limitations of this study

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Systematic impact evaluation syntheses are a vital tool to critically evaluate and plan future work of organisations such as UNICEF; but they are often not feasible due to the size, structure, and amount of evaluation report documents.

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To increase feasibility of analysis we describe a semi-automated human-in-the-loop system which was applied in a synthesis of 631 evaluations across 64 outcomes.

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The proposed open-source code and methods made an evaluation synthesis feasible by reducing text and streamlining the identification of relevant reports for each outcome.

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By making code open-source and adaptable we aim to encourage accelerated, yet transparent and reproducible results.

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While the methods cannot produce 100% complete or correct results for each outcome, they present useful automation methods for researchers facing otherwise non-feasible evaluation syntheses tasks.