Perspectives on reporting non-verbal interactions from the contemporary research focus group
Bibliographic record
Abstract
BACKGROUND: The main defining attribute that delineates focus groups from other methods of collecting data is that data are generated through participants communicating with each other rather than solely with the group moderator. The way in which interactions take place across group interviews and focus groups varies, yet both are referred to as focus groups, resulting in a broad umbrella term for its numerous manifestations. AIM: To reflect on how focus groups are adopted and reported, including the use of the term 'focus group'. DISCUSSION: The authors recognise that the term 'focus group' is sometimes used synonymously with 'group interview' but argue that this practice must be challenged. They suggest using terms that indicate the type of space and synchronicity of the focus group, prefixed with 'in-person' or 'conventional' to identify traditional focus groups. They also suggest separating virtual group interviews into 'synchronous' and 'asynchronous', based on whether the participants and researchers can engage with each other in real time. CONCLUSION: There is a need for qualitative researchers to reach a consensus about the nature of focus groups and group interviews, as well as where their differences and similarities lie. IMPLICATIONS FOR PRACTICE: The authors hope to encourage nurse researchers to think about these issues when labelling, planning, analysing and reporting studies involving focus groups.
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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 | 0.397 | 0.425 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.022 | 0.109 |
| Scholarly communication | 0.039 | 0.037 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".