Conducting qualitative research on cervical cancer screening among diverse groups of immigrant women: research reflections: challenges and solutions.
Bibliographic record
Abstract
OBJECTIVE: To explore the research lessons learned in the process of conducting qualitative research on cervical cancer screening perspectives among multiple ethnolinguistic groups of immigrant women and to provide guidance to family medicine researchers on methodologic and practical issues related to planning and conducting focus group research with multiple immigrant groups. DESIGN: Observations based on a qualitative study of 11 focus groups. SETTING: Hamilton, Ont. PARTICIPANTS: Women from 1 of 5 ethnolinguistic immigrant groups and Canadian-born women of low socioeconomic status. METHODS: We conducted 11 focus groups using interactive activities and tools to learn about women's views of cervical cancer screening, and we used our research team reflections, deliberate identification of preconceptions or potential biases, early and ongoing feedback from culturally representative field workers, postinterview debriefings, and research team debriefings as sources of information to inform the process of such qualitative research. MAIN FINDINGS: Our learnings pertain to 5 areas: forming effective research teams and community partnerships; culturally appropriate ways of accessing communities and recruiting participants; obtaining written informed consent; using sensitive or innovative data collection approaches; and managing budget and time requirements. Important elements included early involvement, recruitment, and training of ethnolinguistic field workers in focus group methodologies, and they were key to participant selection, participation, and effective groups. Research methods (eg, recruitment approaches, inclusion criteria) needed to be modified to accommodate cultural norms. Recruitment was slower than anticipated. Acquiring signed consent might also require extra time. Novel approaches within focus groups increased the likelihood of more rich discussion about sensitive topics. High costs of professional translation might challenge methodologic rigour (eg, back-translation). CONCLUSION: By employing flexible and innovative approaches and including members of the participating cultural groups in the research team, this project was successful in engaging multiple cultural groups in research. Our experiences can inform similar research by providing practical learning within the context of established qualitative methods.
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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 | 0.162 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".