From Small Ripples to a Sea Change: Elucidating Long-Term and Multi-Level Youth Mental Health Intervention Impacts Using Ripple Effects Mapping
Bibliographic record
Abstract
Ripple effects mapping (REM), a qualitative participatory approach to intervention evaluation, is gaining recognition as a useful method for elucidating the long-term intended and unintended impacts of complex public health interventions. The present study applied an adapted REM approach to capture systems and community change associated with the Agenda Gap program. This population-level youth mental health promotion intervention is embedded in multi-sectoral partnerships with long-term and relational outcomes post-program that are difficult to elucidate using traditional program evaluation methods. Using transcript and mind map data generated through an REM process with former Agenda Gap youth collaborators and adult allies, reflexive thematic analysis supported the construction of three thematic program outcomes: (1) Reimagining Future Possibilities, (2) Systems Integration: Transforming School Practices, and (3) Progressing From Ripple Effects to Sea Change. Spanning socioecological levels (i.e., individual, family, community, and societal), the outcomes and their associated sub-themes captured the meaningful impacts experienced by Agenda Gap participants, as well as those more distal to the intervention, in the years following implementation. These findings demonstrate the substantive, multi-level impacts of the program and also illustrate how qualitative, participatory approaches, such as REM, can complement other forms of evaluation to reveal outcomes that are typically overlooked. Recommendations and implications for future research and applications of REM are offered.
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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 | 0.043 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".