Voices Within: Undergraduate Students’ Journey in Ethnocultural Qualitative Research
Bibliographic record
Abstract
Collaborating in qualitative research with immigrant older women is a rich and rewarding learning opportunity for novice researchers as it expands their understanding of the lived experiences of ethnocultural communities. Undergraduate students with the same ethnocultural and linguistic backgrounds as research participants can build trust and rapport in research settings and, hence, are often hired onto research teams to assist with research activities. Undergraduate students learn in the field via hands-on experience and introductory theoretical training in qualitative methods, yet there is little guidance on effective mentorship strategies and considerations for this group of novice researchers. This paper reflects on the experiences of two undergraduate student researchers (USRs) who were involved as research assistants in a qualitative research study on immigrant women’s experiences of aging in place in urban Canadian neighbourhoods. The undergraduate students learned to manage challenges in the field related to setting boundaries and emotional well-being, navigating the presence of family during interviews, and addressing the hesitancy of participants in the research process. This paper discusses strategies to enhance the qualitative research knowledge and participation of USRs who conduct research as insiders in ethnocultural communities. Knowledge generated from this paper will be useful in spurring forward the discussion on qualitative fieldwork training for undergraduate students.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".