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Record W4391530073 · doi:10.46743/2160-3715/2024.6266

Visual Methods for Qualitative Research with Older Refugees: Reflection on Strengths and Drawbacks

2024· article· en· W4391530073 on OpenAlexaff
Prince Chiagozie Ekoh, Christine A. Walsh

Bibliographic record

VenueThe Qualitative Report · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsQualitative researchRefugeeVisual researchPsychologyVisual methodsPhoto elicitationReflection (computer programming)PhotovoiceSociologyEpistemologyCognitive scienceSocial scienceComputer sciencePolitical scienceVisual artsAnthropology

Abstract

fetched live from OpenAlex

Although older refugees have similar characteristics and vulnerabilities as other populations that have benefited from visual research methods, there is scarce evidence of these methods being used for qualitative social work studies with older refugees. In this article, we discuss the application of visual methods for social work research with older refugees for a better understanding of their experiences and challenges, highlighting how it can be evocative, improve discussions by reaching senses that ordinary interviews cannot grasp, avoid re-traumatisation during research, and build resilience and coping in older refugees. We propose that visual methods could address power dynamics that privilege researchers over participants like older refugees, thus empowering and ensuring older refugees control research processes. This can be crystallised by using visual methods in participatory research. We also outline the drawbacks and concerns of using these methods for research with this population. We conclude by presenting a discourse on how visual methods can advance social work knowledge for practice and social change.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
opusno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.142
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1420.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
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 head0.741
GPT teacher head0.832
Teacher spread0.091 · 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

Labeled directly by 3 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Theoretical or conceptual
DomainMethods
GenreMethods

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

Citations2
Published2024
Admission routes1
Has abstractyes

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