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Record W4301394791 · doi:10.52547/johepal.3.3.71

Emotional Vulnerability in Researchers Conducting Trauma-Triggering Research

2022· article· en· W4301394791 on OpenAlexaboutno aff
Sarah Woods, Tina-Nadia Gopal Chambers, Ardavan Eizadirad

Bibliographic record

VenueJournal of Higher Education Policy And Leadership Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)PsychologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

Qualitative researchers prioritize rapport-building to ensure safety of research participants and validity of data collected. Although there is extensive literature about prioritizing the safety and emotional well-being of research participants, much less has been written on the topic of researcher vulnerability with lack of consideration for researcher safety within ethics approval applications. The authors present a reflexive account of a research project involving interviews with young people aged 15 to 30 in Toronto, Canada who had firearm related charges. The methodological, ethical issues, and research burnout and vulnerability that arose due to the shared lived experience between the principal researcher and the research participants are discussed. Overall, the article explores the complexities and nuances involved when conducting research with topics that may be trauma-triggering and can contribute to researcher burnout and compassionate fatigue. It is argued that researchers are not immune to these risk factors and due to such exposure may experience depression and other negative side effects. Series of suggestions are outlined to reduce harm exposure for researchers and to improve how they can better be supported to cope and heal from conducting trauma-triggering research before, during, and after completion of a research project.

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)0.045
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.933
GPT teacher head0.706
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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