Qualitative Story Completion: Opportunities and Considerations for Health Research
Bibliographic record
Abstract
Qualitative story completion (QSC) is an innovative research method that offers researchers a range of unique opportunities for generating and analysing data. Participants are asked to write a ‘story’ in response to a hypothetical ‘story stem’, often in the third-person and involving fictional characters, rather than reporting on their direct experiences. QSC is being developed and increasingly taken up by researchers working across a range of fields; but it has been little used in health research, especially in the fields of nursing, health services research, medicine, and allied health. This means that health researchers have few examples to draw on when considering what QSC can offer them and how to rigorously design, conduct, and report a QSC study within health-related fields. We aim to address this gap and contribute to existing QSC literature by promoting increased use of QSC by health researchers and supporting them to produce rigorous QSC research. We outline three case examples illustrating how we have used QSC to conduct multidisciplinary health research relevant to nursing, medicine and nutrition. Drawing on these case examples, we reflect on challenges that we encountered, describe decision-making processes, and offer recommendations for conducting rigorous health research using QSC.
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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 | 0.546 | 0.515 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.034 | 0.044 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".