Lessons From a Theory of Change-driven Evaluation of a Global Mental Health Funding Portfolio
Bibliographic record
Abstract
Abstract BackgroundGiven the underinvestment in global mental health to-date, it is important to consider how best to maximize the impact of existing investments. Theory of Change (ToC) is increasingly attracting the interest of funders seeking to evaluate their own impact. This is the first of four papers investigating Grand Challenges Canada’s (GCC’s) first global mental health research funding portfolio (2012-2016) using a ToC-driven approach.MethodsA portfolio-level ToC map was developed through a collaborative process involving GCC grantees and other key stakeholders. Proposed ToC indicators were harmonised with GCC’s pre-existing Results-based Management and Accountability Framework to produce a “Core Metrics Framework” of 23 indicators linked to 17 outcomes of the ToC map. For each indicator relevant to their project, the grantee was asked to set a target prior to the start of implementation, then report results at six-month intervals. We used the latest available dataset from all 56 projects in GCC’s global mental health funding portfolio to produce a descriptive analysis of projects’ characteristics and outcomes related to delivery. Results12,999 people were trained to provide services, the majority of whom were lay or other non-specialist health workers. Most projects exceeded their training targets for capacity-building, except for those training lay health workers. Of the 321,933 people screened by GCC-funded projects, 162,915 received treatment. Most projects focused on more than one disorder and exceeded all their targets for screening, diagnosis and treatment. Fewer people than intended were screened for common mental disorders and epilepsy (60% and 54%, respectively), but many more were diagnosed and treated than originally proposed (148% and 174%, respectively). In contrast, the three projects that focused on perinatal depression exceeded screening and diagnosis targets, but only treated 43% of their intended target. ConclusionsUnder- or over-achievement of targets may reflect operational challenges such as high staff turnover, or challenges in setting appropriate targets, for example due to insufficient epidemiological evidence. Differences in delivery outcomes when disaggregated by disorder suggest that these challenges are not universal. We caution implementers, funders and evaluators from taking a one-size-fits all approach and make several recommendations for how to facilitate more in-depth, multi-method evaluation of impact using portfolio-level ToC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.190 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".