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Record W2944091551 · doi:10.1177/1609406919843022

Methodological Challenges Faced in Doing Research With Vulnerable Women: Reflections From Fieldwork Experiences

2019· article· en· W2944091551 on OpenAlexaff
Onouma Thummapol, Tanya Park, Margot Jackson, Sylvia Barton

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Northern British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsConfidentialityField (mathematics)Engineering ethicsQualitative researchProcess (computing)SociologyPublic relationsPsychologyPolitical scienceSocial scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Methodological challenges of qualitative research involving people considered vulnerable are widely prevalent, for which many novice researchers are not well equipped or prepared for. This places great physical and emotional demands on the researchers. However, a discussion to bring to light the issues related to the researchers’ experiences and practical concerns in the field remains largely invisible in the literature. This article presents the reflective accounts of a doctoral researcher’s fieldwork experience, particularly in relation to the methodological challenges encountered in carrying out research with vulnerable women in rural and northern Thailand. Four of these challenges pertain to selecting a field site and acquiring access, recruiting and building trust, maintaining privacy and confidentiality, and being vulnerable as a researcher. Suggestions from the literature and practical strategies the researcher employed to deal with such challenges and real dilemmas are discussed. This article calls for more formal safeguards during the research process and suggests that researchers reflect upon their experiences and emotions in undertaking a field research, making the accounts of their research journey heard and beneficial to other novice and/or experienced researchers.

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)0.189
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.368
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1890.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.956
GPT teacher head0.789
Teacher spread0.167 · 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
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

Citations46
Published2019
Admission routes1
Has abstractyes

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