Two Approaches to Focus Group Data Collection for Qualitative Health Research
Bibliographic record
Abstract
This article discusses four challenges to conducting qualitative focus groups: (1) maximizing research budgets through innovative methodological approaches, (2) recruiting health-care professionals for qualitative health research, (3) conducting focus groups with health-care professionals across geographically dispersed areas, and (4) taking into consideration data richness when using different focus group data collection methods. In light of these challenges, we propose two alternative approaches for collecting focus group data: (a) extended period of quantitative data collection that facilitated relationship building in the sites prior to qualitative focus groups and (b) focus groups by videoconference. We share our experiences on employing both of these approaches in two national research programs.
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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 | 0.255 | 0.343 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".