Principles and Elements for Creating and Sustaining Successful PPP for Environmental Community Monitoring Programs: Results from a Scoping Review and Interviews
Bibliographic record
Abstract
Introduction: Public–private partnerships (PPPs) can play a critical role in advancing our understanding of environmental exposures by maximizing cross-disciplinary expertise and resource-sharing among government, community, and industry researchers. However, experiences with PPPs associated with community environmental monitoring involving industry partners have not been well-documented. Goal: To build on existing literature combined with the expertise of practitioners from various sectors to identify overarching elements and specific principles necessary for creating and sustaining successful PPPs for community environmental monitoring. Methods: A scoping literature review and 24 semistructured interviews with diverse PPP stakeholders were conducted. Excerpts from the review were coded to successful/barrier elements. Interview transcripts were analyzed using content analysis. Results: Trust emerged as a foundational principle both in the literature review and in interviews. After trust is developed, three critical principles for successful PPPs are: a sound organizational structure with sufficient resources to maintain the PPP, clear and inclusive approaches to communication, and developing scientifically robust data as the basis for decision-making. Conclusions: Community interviewees realized the value of PPPs but engaged in them cautiously given power imbalances and prior negative experiences. Our analyses confirm that historic events and power imbalances affect trust and participation of community partners, and that trust-building is a continuous process requiring honesty, bidirectional communication, sustained presence, and acknowledgment of prior activities adversely impacting the environment. A centralized repository or a professional community society would facilitate the sharing of lessons learned. PPPs may benefit by including trained facilitators for equitable and participatory processes.
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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 | 0.188 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".