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Record W2123793600 · doi:10.1177/1473325014566842

Contribution of participatory action research to knowledge mobilization in mental health services for children and families

2015· article· en· W2123793600 on OpenAlexafffund
Michael Ungar, Patrick J. McGrath, David Black, Ingrid Sketris, Shelly Whitman, Linda Liebenberg

Bibliographic record

VenueQualitative Social Work · 2015
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersNetworks of Centres of Excellence of CanadaSocial Sciences and Humanities Research Council of Canada
KeywordsParticipatory action researchCitizen journalismParticipatory evaluationPublic relationsCommunity-based participatory researchMental healthAction (physics)Knowledge sharingBest practiceSociologyPsychologyKnowledge managementMedical educationPolitical scienceMedicineComputer scienceSocial science

Abstract

fetched live from OpenAlex

Problems with knowledge mobilization (KMb) (also known as knowledge translation and implementation science) among providers of children and youth services may be addressed by looking to models of participatory action research (PAR) that are already familiar to those working in community-based services. In contexts such as these, where there is mistrust of traditional sources of expertise, PAR has the potential to provide a way to make it easier for the sharing and adoption of new practices. A case example of an evaluation of a community-based gang prevention program for children aged 9–14 is used to highlight how PAR can enhance program design and implementation based on the sharing of best practices and the active engagement of community members through a research advisory committee. This integration of PAR with KMb, though an imperfect attempt to share practice evidence, provides clues to the methodological techniques required for more participatory development and exchange of promising practices among providers of services for children and youth.

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)0.275
metaresearch head score (Gemma)0.247
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2750.247
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.004
Science and technology studies0.0130.054
Scholarly communication0.0210.020
Open science0.0060.027
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.617
GPT teacher head0.643
Teacher spread0.026 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations31
Published2015
Admission routes2
Has abstractyes

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