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Development of a child-centred community-based participatory research approach to injury prevention

2022· article· en· W4221123476 on OpenAlexafffund
Michelle E. E. Bauer, Ian Pike

Bibliographic record

VenueInjury Prevention · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCommunity-based participatory researchParticipatory action researchCitizen journalismPoison controlSuicide preventionHuman factors and ergonomicsInjury preventionOccupational safety and healthPsychologySociologyPublic relationsMedical educationNursingMedicinePolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Community-based participatory research (CBPR) approaches to injury prevention are conducted so as to foster inclusiveness and collaboration in research processes and settings. Despite the benefits of using CBPR approaches to represent voices in research that are typically marginalised, they are overwhelmingly used in collaborations with youth and adults. Developing a child-centred CBPR approach can serve the important purpose of fostering awareness for children's voices and needs in injury prevention, and can help future researchers engage communities of children in a genuine and respectful way. PURPOSE: To develop a four-staged model of a child-centred CBPR approach to injury prevention and outline the development.

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)0.034
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.761
GPT teacher head0.639
Teacher spread0.122 · 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 designNot applicable
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

Citations5
Published2022
Admission routes2
Has abstractyes

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