Applying the results based management framework to the CERCA multi-component project in adolescent sexual and reproductive health: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Adolescent sexual and reproductive health (SRH), problems such as unplanned pregnancies are complex and multifactorial, thus requiring multifaceted prevention interventions. Evaluating the impact of such interventions is important to ensure efficiency, effectiveness and accountability for project funders and community members. In this study, we propose Results Based Management (RBM) as a framework for project management, using the Community Embedded Reproductive Health Care for Adolescents (CERCA) as a case study for RBM. The CERCA Project (2010-2014) tested interventions to reduce adolescent pregnancy in three Latin American countries, Bolivia, Ecuador and Nicaragua. Activities were designed to increase adolescent SRH behaviors in four domains: communication with parents, partners and peers; access to SRH information; access to SRH services; and use of contraception. When the project ended, the outcome evaluation showed limited impact with concerns about accuracy of monitoring and attrition of participants. METHODS: We reviewed and analyzed a series of CERCA documents and related data sources. Key findings from these documents were organized within an RBM framework (planning, monitoring, and impact evaluation) to understand how CERCA methodology and performance might have reaped improved results. RESULTS: Strengths and weaknesses were identified in all three elements of the RBM framework. In Planning, the proposed Theory of Change (ToC) differed from that which was carried out in the intervention package. Each country implemented a different intervention package without articulated assumptions on how the activities of intervention would bring about change. In Monitoring, the project oversight was mainly based on administrative and financial requirements rather than monitoring fidelity and quality of intervention activities. In Impact Evaluation, the original CERCA evaluation assessed intervention effects among adolescents, without identifying success and failure factors related to the outcomes, the nature of the outcomes, or cost-effectiveness of interventions. CONCLUSIONS: This analysis showed that multi-country projects are complex, entail risks in execution and require robust project management. RBM can be a useful tool to ensure a systematic approach at different phases within a multi-country setting.
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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 | 0.116 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".