MétaCan
Menu
Back to cohort
Record W2794681790

Key Components of Collaborative Research in the Context of Environmental Health: A Scoping Review

2017· review· en· W2794681790 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of research practice · 2017
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCarleton UniversityUniversity of Alberta
Fundersnot available
KeywordsKnowledge translationContext (archaeology)Key (lock)Knowledge managementProcess (computing)Computer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.244
metaresearch head score (Gemma)0.086
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2440.086
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.008
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.960
GPT teacher head0.842
Teacher spread0.118 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it