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Record W4297830886 · doi:10.1080/14780887.2022.2107967

Toward a trauma-informed qualitative research approach: Guidelines for ensuring the safety and promoting the resilience of research participants

2022· article· en· W4297830886 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQualitative Research in Psychology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsMcGill University
Fundersnot available
KeywordsQualitative researchFocus groupPsychologyPsychological resilienceParticipant observationTransgenderMedical educationSocial psychologySociologyMedicine

Abstract

fetched live from OpenAlex

Qualitative researchers frequently conduct studies with individuals who have experienced various types of trauma, including those who have been historically marginalized and oppressed. However, in-depth discussions of how to conduct trauma-informed qualitative research do not exist. Thus, we lay the groundwork for a trauma-informed qualitative approach and then outline five guidelines for conducting research: (1) preparing for community entry: Learning about the impacts of traumatic events and historical trauma on individuals and communities; (2) preparing for the qualitative interview or focus group: Establishing safety and trust in the research environment; (3) extending safety and trust into the qualitative interview or focus group; (4) knowing when to change course to avoid re-traumatization in the interview or focus group; and (5) committing to regular and radical self-reflection and self-care in the research process. To demonstrate their applicability, we use an example from our own research with multiply-marginalized queer and transgender migrants in South Africa. This article advances the study of qualitative methods, offering researchers an opportunity to incorporate these guidelines into their study design and implementation to ensure participant safety and promote their resilience.

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.

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
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
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)0.656
metaresearch head score (Gemma)0.215
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6560.215
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.007
Science and technology studies0.0080.027
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.006
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 head0.935
GPT teacher head0.793
Teacher spread0.141 · 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