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Record W3193646413 · doi:10.1177/16094069211036293

Engaging Immigrant and Racialized Communities in Community-Based Participatory Research During the COVID-19 Pandemic: Challenges and Opportunities

2021· article· en· W3193646413 on OpenAlexaffabout
Jordana Salma, Deena Giri

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCommunity-based participatory researchParticipatory action researchPandemicImmigrationSociologySocial distanceContext (archaeology)Vulnerability (computing)Public relationsPhotovoiceCoronavirus disease 2019 (COVID-19)Political scienceEconomic growthMedicineGeography

Abstract

fetched live from OpenAlex

Community-based participatory research (CBPR) approaches have been important avenues for addressing community vulnerability during pandemics and times of crises. There has been little guidance, however, on how to approach CBPR within the context of the COVID-19 pandemic where physical distancing and closure of essential community organizations became the norm. This study discusses challenges and possibilities of using CBPR during a pandemic to address the needs of immigrant and racialized older adults in Alberta, Canada. Two case studies of active research projects that aim to engage immigrant and racialized older adults are presented. Three key challenges are identified related to research activities during the pandemic: (a) pivoting as new foci emerge, (b) recognizing inequity in research participation, and (c) reflecting on well-being in the research team. Approaches to addressing these challenges are highlighted with recommendations for future considerations in CBPR research within vulnerable communities.

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 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)0.077
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0770.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.977
GPT teacher head0.750
Teacher spread0.227 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
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

Citations42
Published2021
Admission routes2
Has abstractyes

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