Combining individual interviews and focus groups to enhance data richness
Bibliographic record
Abstract
AIM: This paper is a presentation of the critical reflection on the types of findings obtained from the combination of individual interviews and focus groups, and how such triangulation contributes to knowledge production and synthesis. BACKGROUND: Increasingly, qualitative method triangulation is advocated as a strategy to achieve more comprehensive understandings of phenomena. Although ontological and epistemological issues pertaining to triangulation are a topic of debate, more practical discussions are needed on its potential contributions, such as enhanced data richness and depth of inquiry. METHOD: Data gathered through individual interviews and focus groups from a study on patterns of cancer information-seeking behaviour are used to exemplify the added-value but also the challenges of relying on methods combination. FINDINGS: The integration of focus group and individual interview data made three main contributions: a productive iterative process whereby an initial model of the phenomenon guided the exploration of individual accounts and successive individual data further enriched the conceptualisation of the phenomenon; identification of the individual and contextual circumstances surrounding the phenomenon, which added to the interpretation of the structure of the phenomenon; and convergence of the central characteristics of the phenomenon across focus groups and individual interviews, which enhanced trustworthiness of findings. CONCLUSION: Although the use of triangulation is promising, more work is needed to identify the added-value or various outcomes pertaining to method combination and data integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.174 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".