Saving Lives at Birth; development of a retrospective theory of change, impact framework and prioritised metrics
Bibliographic record
Abstract
BACKGROUND: Grand Challenges for international health and development initiatives have received substantial funding to tackle unsolved problems; however, evidence of their effectiveness in achieving change is lacking. A theory of change may provide a useful tool to track progress towards desired outcomes. The Saving Lives at Birth partnership aims to address inequities in maternal-newborn survival through the provision of strategic investments for the development, testing and transition-to-scale of ground-breaking prevention and treatment approaches with the potential to leapfrog conventional healthcare approaches in low resource settings. We aimed to develop a theory of change and impact framework with prioritised metrics to map the initiative's contribution towards overall goals, and to measure progress towards improved outcomes around the time of birth. METHODS: A theory of change and impact framework was developed retrospectively, drawing on expertise across the partnership and stakeholders. This included a document and literature review, and wide consultation, with feedback from stakeholders at all stages. Possible indicators were reviewed from global maternal-newborn health-related partner initiatives, priority indicator lists, and project indicators from current innovators. These indicators were scored across five domains to prioritise those most relevant and feasible for Saving Lives at Birth. These results informed the identification of the prioritised metrics for the initiative. RESULTS: The pathway to scale through Saving Lives at Birth is articulated through a theory of change and impact framework, which also highlight the roles of different actors involved in the programme. A prioritised metrics toolkit, including ten core impact indicators and five additional process indicators, complement the theory of change. The retrospective nature of this development enabled structured reflection of the program mechanics, allowing for inclusion of learning from the first four rounds of the program to inform implementation of subsequent rounds. CONCLUSIONS: While theories of change are more traditionally developed before program implementation, retrospective development can still be a useful exercise for multi-round programs like Saving Lives at Birth, where outputs from the development can be used to strengthen subsequent rounds. However, identifying a uniform set of prioritised metrics for use across the portfolio proved more challenging. Lessons learnt from this exercise will be relevant to the development of pathways to change across other Grand Challenges and global health platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".