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Record W2196782522 · doi:10.9745/ghsp-d-15-00221

Monitoring and Evaluating the Transition of Large-Scale Programs in Global Health

2015· article· en· W2196782522 on OpenAlex
James Bao, Daniela Rodríguez, Ligia Paina, Sachiko Ozawa, Sara Bennett

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health Science and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Toronto
FundersUnited States Agency for International Development
KeywordsStakeholderTransition (genetics)Process managementSustainabilityTransition management (governance)Stakeholder engagementMonitoring and evaluationSet (abstract data type)BusinessKnowledge managementComputer sciencePublic relationsPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: Donors are increasingly interested in the transition and sustainability of global health programs as priorities shift and external funding declines. Systematic and high-quality monitoring and evaluation (M&E) of such processes is rare. We propose a framework and related guiding questions to systematize the M&E of global health program transitions. METHODS: We conducted stakeholder interviews, searched the peer-reviewed and gray literature, gathered feedback from key informants, and reflected on author experiences to build a framework on M&E of transition and to develop guiding questions. FINDINGS: The conceptual framework models transition as a process spanning pre-transition and transition itself and extending into sustained services and outcomes. Key transition domains include leadership, financing, programming, and service delivery, and relevant activities that drive the transition in these domains forward include sustaining a supportive policy environment, creating financial sustainability, developing local stakeholder capacity, communicating to all stakeholders, and aligning programs. Ideally transition monitoring would begin prior to transition processes being implemented and continue for some time after transition has been completed. As no set of indicators will be applicable across all types of health program transitions, we instead propose guiding questions and illustrative quantitative and qualitative indicators to be considered and adapted based on the transition domains identified as most important to the particular health program transition. The M&E of transition faces new and unique challenges, requiring measuring constructs to which evaluators may not be accustomed. Many domains hinge on measuring "intangibles" such as the management of relationships. Monitoring these constructs may require a compromise between rigorous data collection and the involvement of key stakeholders. CONCLUSION: Monitoring and evaluating transitions in global health programs can bring conceptual clarity to the transition process, provide a mechanism for accountability, facilitate engagement with local stakeholders, and inform the management of transition through learning. Further investment and stronger methodological work are needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.487
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it