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Record W4381853102 · doi:10.1177/16094069231183606

Qualitative Comic Book Mapping: Developing Comic Books Informed by Lived Experiences of Refugee Youth to Advance Sexual and Gender-Based Violence Prevention and Stigma Reduction in a Humanitarian Setting in Uganda

2023· article· en· W4381853102 on OpenAlexafffund
Carmen H. Logie, Moses Okumu, Alyssa McAlpine, Simon Odong Lukone, Nelson Kisubi, Miranda G. Loutet, Isha Berry, F. Mackenzie, Peter Kyambadde

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsPublic Health OntarioUnited Nations University Institute for Water, Environment, and HealthWomen's College HospitalUniversity of Toronto
FundersGrand Challenges CanadaCanada Foundation for InnovationSocial Sciences and Humanities Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Research Chairs
KeywordsComicsRefugeeQualitative researchThematic analysisStigma (botany)Sexual violencePsychological interventionReproductive healthPsychologySociologyMedicinePolitical scienceNursingPopulationCriminologyPsychiatrySocial scienceEnvironmental health

Abstract

fetched live from OpenAlex

Sexual and gender-based violence (SGBV) is a persistent concern in humanitarian contexts, yet there is a dearth of SGBV prevention and post-rape clinical care interventions tailored for refugee youth. Graphic medicine, the use of images and text such as in comic books, has been employed to depict lived experiences to promote health, wellbeing, and education. Comic books provide a low-cost, youth-friendly approach to health promotion that is accessible to varying literacy levels. Limited research, however, has described the process of developing graphic medicine approaches for SGBV prevention and sexual violence stigma reduction with and for refugee youth in humanitarian settings. To address this knowledge gap, this paper shares a Qualitative Comic Book Mapping approach, whereby qualitative data alongside theoretical and empirical SGBV literature informed the development of comic book scenarios with refugee youth aged 16-24 in Bidi Bidi refugee settlement, Uganda. Steps included conducting focus groups and in-depth individual interviews with 78 community members (youth, elders, service providers) in Bidi Bidi to explore SGBV lived experiences among refugee youth in Bidi Bidi and ideas for solutions to reduce SGBV and related stigma, in addition to improving post-rape care experiences and engagement. The Qualitative Comic Book Mapping approach involved: a) thematic analysis of qualitative data and identification of overarching themes; b) aligning qualitative themes with theories of change for SGBV prevention and stigma reduction; and c) co-developing comic book scenarios with refugee youth peer navigators and community experts to integrate SGBV prevention and stigma reduction theory with refugee youth lived experiences. The final comic book involved five youth-focused scenarios and was integrated in an intervention with refugee youth, including providing youth with a blank version of the comic book to complete themselves. We share how theoretically-informed comic books can be developed from qualitative data with refugee youth in a humanitarian setting.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.588
GPT teacher head0.647
Teacher spread0.059 · 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.

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".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

Explore more

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