Scenario planning for community development in Vietnam: a new tool for integrated health approaches?
Bibliographic record
Abstract
BACKGROUND: Like many countries in Southeast Asia, Vietnam's rapid population and economic growth has met challenges in infrastructure development, especially sanitation in rural areas. OBJECTIVE: As an entry point, we developed scenario planning as an action-research tool in a peri-urban community to identify first steps towards improving their complex sanitation problem and to, systemically, address emerging/re-emerging infectious diseases, as these are commonly linked to unsafe water and inadequate sanitation conditions. As an integrated approach, the process of constructing scenarios allowed us to work across sectors and stakeholders to incorporate this knowledge into a common vision. DESIGN: We conducted focus group discussions to identify and rank driving forces, orally constructed scenarios for the most uncertain drivers, discussed scenario implications and options, and examined the overall process for usefulness and sustainability. During a one-month scoping phase and in between focus group meetings, we carried out household visits which helped us understand the context of data and gather feedback from participants outside of the formal data collection process. Recorded results from these activities were used to develop subsequent tools. RESULTS AND CONCLUSIONS: The research process gave us insights into how to adapt the scenario planning tool to identify alternative options. This involved choosing boundary partners, negotiating priorities, drawing out participant learning through self-assessment of our process (a prerequisite for changing mental models and thus achieving outcomes), and understanding how conveyed messages may reinforce the status quo. These insights showed the importance of examining research results beyond outputs and outcomes, namely through process.
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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 | 0.056 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".