Modifications of a Parenting Program in the Context of Scaling-Up and Scaling-Out: Documenting Furaha Teens in Tanzania Using FRAME
Bibliographic record
Abstract
Program adaptations or modifications are often necessary to suit local contexts, populations, and resources available. Despite the frequency with which program modifications are made in practice, they are rarely systematically recorded and reported comprehensively, particularly in the context of scale-up delivery led by implementers and in low- and middle-income countries. We use the FRAME framework to document the modifications of a parenting program called Parenting for Lifelong Health for Parents and Adolescents, locally known as Furaha Teens, which was delivered to over 30,000 families in Tanzania in 2020-2021. We draw on thematic analysis of 12 focus groups and 67 semi-structured interviews with program facilitators, coaches, coordinators, and managers (164 participants). Both proactive and reactive modifications were made to the program context and content. Proactive modifications included delivering the program as part of a wider package of services for families with adolescent girls, focused on HIV prevention, and adding HIV-related content. Both proactive and reactive modifications were made to make the material more acceptable to participants, such as by translating into local languages. Modifications to condense the number and frequency of sessions were reactively made by implementers to meet delivery timelines, particularly due to COVID-related closures. Study findings suggest that a range of program modifications may be required to scale programs to large cohorts as well as new contexts. To ensure successful delivery at scale, funders can support implementers in learning from the modifications and encouraging reflection on whether and how modifications affect program fidelity.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".