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Community‐based Participatory Research: Development of an Emergency Department–based Youth Violence Intervention Using Concept Mapping

2010· article· en· W2113096920 on OpenAlexafffundabout
Maritt Kirst, Shakira Abubakar, Farah Ahmad, Avery B. Nathens

Bibliographic record

VenueAcademic Emergency Medicine · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)University of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsTeachable momentMedicineBrainstormingIntervention (counseling)Psychological interventionEmergency departmentParticipatory action researchMedical educationQualitative researchNursingPsychologySociology

Abstract

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OBJECTIVES: Emergency departments (EDs) see a high number of youths injured by violence. In Ontario, the most common cause of injury for youths visiting EDs is assault. Secondary prevention strategies using the teachable moment (i.e., events that can lead individuals to make positive changes in their lives) are ideal for use by clinicians. An opportunity exists to take advantage of the teachable moment in the ED in an effort to prevent future occurrences of injury in at-risk youths. However, little is known about perceptions of youths, parents, and community organizations about such interventions in EDs. The aims of this study were to engage youths, parents, and frontline community workers in conceptualizing a hospital-based violence prevention intervention and to identify outcomes relevant to the community. METHODS: Concept mapping is an innovative, mixed-method research approach. It combines structured qualitative processes such as brainstorming and group sorting, with various statistical analyses such as multidimensional scaling and hierarchical clustering, to develop a conceptual framework, and allows for an objective presentation of qualitative data. Concept mapping involves multiple structured steps: 1) brainstorming, 2) sorting, 3) rating, and 4) interpretation. For this study, the first three steps occurred online, and the fourth step occurred during a community meeting. RESULTS: Over 90 participants were involved, including youths, parents, and community youth workers. A two-dimensional point map was created and clusters formed to create a visual display of participant ideas on an ED-based youth violence prevention intervention. Issues related to youth violence prevention that were rated of highest importance and most realistic for hospital involvement included mentorship, the development of youth support groups in the hospital, training doctors and nurses to ask questions about the violent event, and treating youth with respect. Small-group discussions on the various clusters developed job descriptions, a list of essential services, and suggestions on ways to create a more youth-friendly environment in the hospital. A large-group discussion revealed outcomes that participants felt should be measured to determine the success of an intervention program. CONCLUSIONS: This study has been the springboard for the development of an ED-based youth violence intervention that is supported by the community and affected youth. Using information generated by youth that is grounded in their experience through participatory research methods is feasible for the development of successful and meaningful youth violence prevention interventions.

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)0.077
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.004
Scholarly communication0.0030.004
Open science0.0040.009
Research integrity0.0020.003
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 head0.536
GPT teacher head0.526
Teacher spread0.010 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations44
Published2010
Admission routes3
Has abstractyes

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