Methodological Challenges Faced in Doing Research With Vulnerable Women: Reflections From Fieldwork Experiences
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.035 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".