MétaCan
Menu
Back to cohort
Record W3153966546 · doi:10.15402/esj.v6i2.70165

Community engagement in Canadian health and social science research: Field reports on four studies

2021· article· en· W3153966546 on OpenAlexafffundvenueabout
Andrew D. Eaton

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoCanadian Institutes of Health ResearchOntario HIV Treatment Network
KeywordsParticipatory action researchCommunity-based participatory researchCommunity engagementScope (computer science)Public relationsContext (archaeology)Citizen journalismSociologyPublic engagementHealth equityPsychologyPolitical sciencePublic healthMedicineNursingComputer science

Abstract

fetched live from OpenAlex

Community engagement is a hallmark of Canadian health and social science research, yet we lack detailed descriptions of pragmatic peer engagement possibilities. People personally affected by a study’s topic can actively contribute to design, data collection, intervention delivery, analysis, and dissemination yet the nature and scope of involvement can vary based on context. The shift from academic to community-based research teams, where peers who share participant identities assume a leadership role, may be attributed to the HIV/AIDS response where community co-production of knowledge has been a fundamental component since the epidemic’s onset. This article discusses four health and social science studies from a community-based participatory research (CBPR) framework and synthesizes the strengths and limitations of community engagement across these endeavours to offer lessons learned that may inform the design of future CBPR projects.

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 imitation

Not 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.

metaresearch head score (Codex)0.123
metaresearch head score (Gemma)0.187
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1230.187
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0110.023
Science and technology studies0.0410.018
Scholarly communication0.0100.004
Open science0.0050.023
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.892
GPT teacher head0.712
Teacher spread0.180 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainMethods
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes4
Has abstractyes

Explore more

Same venueEngaged Scholar Journal Community-Engaged Research Teaching and LearningSame topicHealth Policy Implementation ScienceFrench-language works237,207