Key Components of Collaborative Research in the Context of Environmental Health: A Scoping Review
Bibliographic record
Abstract
In a collaborative research process, the participation of interdisciplinary researchers and multi-sectoral stakeholders supports the co-creation, translation, and exchange of new knowledge. Following a scoping review methodology, we explored the collaborative research processes in the specific context of environment and human health research. Initially, our literature search strategy identified 1,328 publications. After several phases of reviewing and applying screening criteria to titles, abstracts, and full text, 45 publications were selected for final review. Data were charted by different topics and then collated, summarized, and analyzed thematically. From the different experiences and research approaches analyzed, we identified comprehensive details of the key components, facilitators, challenges, and best practices that impact the collaborative research process. Specifically, we identified the following seven emerging themes: (a) allocating time and resources, (b) addressing disciplinary and sectoral issues, (c) building relationships, (d) ensuring representation, (e) embedding participation in the research, (f) supporting ongoing collaboration, and (g) developing knowledge translation and exchange.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.123 | 0.232 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.045 | 0.045 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".