‘Trying to pin down jelly’ - exploring intuitive processes in quality assessment for meta-ethnography
Bibliographic record
Abstract
BACKGROUND: Studies that systematically search for and synthesise qualitative research are becoming more evident in health care, and they can make an important contribution to patient care. However, there is still no agreement as to whether, or how we should appraise studies for inclusion. We aimed to explore the intuitive processes that determined the 'quality' of qualitative research for inclusion in qualitative research syntheses. We were particularly interested to explore the way that knowledge was constructed. METHODS: We used qualitative methods to explore the process of quality appraisal within a team of seven qualitative researchers funded to undertake a meta-ethnography of chronic non-malignant musculoskeletal pain. Team discussions took place monthly between October 2010 and June 2012 and were recorded and transcribed. Data was coded and organised using constant comparative method. The development of our conceptual analysis was both iterative and collaborative. The strength of this team approach to quality came from open and honest discussion, where team members felt free to agree, disagree, or change their position within the safety of the group. RESULTS: We suggest two core facets of quality for inclusion in meta-ethnography - (1) Conceptual clarity; how clearly has the author articulated a concept that facilitates theoretical insight. (2) Interpretive rigour; fundamentally, can the interpretation 'be trusted?' Our findings showed that three important categories help the reader to judge interpretive rigour: (ii) What is the context of the interpretation? (ii) How inductive is the interpretation? (iii) Has the researcher challenged their interpretation? CONCLUSIONS: We highlight that methods alone do not determine the quality of research for inclusion into a meta-ethnography. The strength of a concept and its capacity to facilitate theoretical insight is integral to meta-ethnography, and arguably to the quality of research. However, we suggest that to be judged 'good enough' there also needs to be some assurance that qualitative findings are more than simply anecdotal. Although our conceptual model was developed specifically for meta-ethnography, it may be transferable to other research methodologies.
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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 | 0.717 | 0.800 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.025 | 0.040 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".