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Record W4308499612 · doi:10.1016/j.ijdrr.2022.103420

Insights on the COVID-19 pandemic: Youth engagement through Photovoice

2022· article· en· W4308499612 on OpenAlexafffund
Christina J. Pickering, Zobaida Al‐Baldawi, Lauren McVean, Munira Adan, Raissa A. Amany, Zaynab Al-Baldawi, Lucy Baker, Tracey O’Sullivan

Bibliographic record

VenueInternational Journal of Disaster Risk Reduction · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsConcordia UniversitySeneca PolytechnicWilfrid Laurier UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPhotovoiceThematic analysisPandemicCommunity resiliencePublic relationsFocus groupParticipatory action researchReflexivitySociologyCommunity engagementPsychological resilienceCoronavirus disease 2019 (COVID-19)Political scienceQualitative researchPsychologyEconomic growthMedicineEngineeringSocial psychologySocial science

Abstract

fetched live from OpenAlex

Youth engagement in disaster risk reduction is a growing area of research, practice and policy. The COVID-19 pandemic highlighted the need for improved opportunities for youth to participate and have their voices heard. Our Photovoice study explores experiences, perceptions, and insights of youth regarding the COVID-19 pandemic, while providing an opportunity for youth to participate in disaster risk reduction and contribute to resilient communities. We conducted nine focus groups from February 2019 to August 2020 with four teenaged youth; we analyzed the data using reflexive thematic analysis and hosted two virtual Photovoice exhibitions. Our results explore youth experiences of public health measures, impacts of the pandemic, pandemic magnification of social inequities, and the power of youth to create change. We provide six calls to action, focusing on a holistic, upstream, all-of-society approach for stakeholders to collaborate with youth in creating change on complex social justice issues to support COVID-19 recovery.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.683
GPT teacher head0.607
Teacher spread0.075 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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