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Record W2777080356 · doi:10.1111/fare.12269

A Cultural‐Variant Approach to Community‐Based Participatory Research: New Ideas for Family Professionals

2017· article· en· W2777080356 on OpenAlexaff
Tammy L. Henderson, Aya Shigeto, James J. Ponzetti, Anne Edwards, Jessica Stanley, Chandra R. Story

Bibliographic record

VenueFamily Relations · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation
KeywordsParticipatory action researchCommunity-based participatory researchGrandparentSociologyPerspective (graphical)Action researchPublic relationsPedagogyPsychologyPolitical science

Abstract

fetched live from OpenAlex

The cultural‐variant community‐based participatory research (CV‐CBPR) model expands the traditional community‐based participatory research (CBPR) model and supports the ongoing creation of innovative basic family and translational science. The CV‐CBPR model supports family professionals using a cultural‐variant perspective that discourages the use of a deficit or pathological lens. It also encourages inclusive and culture‐sensitive practices in all stages of a project. After a brief review of diverse types of community or action‐research projects and the nine principles of the traditional CBPR model, a cultural‐variant perspective and related principles are described. We offer lessons learned from two project management experiences: a community‐focused, disaster project with older survivors of Hurricane Katrina and a CBPR arctic‐climate project with Alaska Native grandparents rearing grandchildren.

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)0.165
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1650.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0140.099
Scholarly communication0.0240.022
Open science0.0070.015
Research integrity0.0080.010
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 head0.952
GPT teacher head0.743
Teacher spread0.209 · 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 designTheoretical or conceptual
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

Citations19
Published2017
Admission routes1
Has abstractyes

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