A scoping review of the potential for chart stimulated recall as a clinical research method
Bibliographic record
Abstract
BACKGROUND: Chart-stimulated recall (CSR) is a case-based interviewing technique, which is used in the assessment of clinical decision-making in medical education and professional certification. Increasingly, clinical decision-making is a concern for clinical research in primary care. In this study, we review the prior application and utility of CSR as a technique for research interviews in primary care. METHODS: Following Arksey & O'Malley's method for scoping reviews, we searched seven databases, grey literature, reference lists, and contacted experts in the field. We excluded studies on medical education or competence assessment. Retrieved citations were screened by one reviewer and full texts were ordered for all potentially relevant abstracts. Two researchers independently reviewed full texts and performed data extraction and quality appraisal if inclusion criteria were met. Data were collated and summarised using a published framework on the reporting of qualitative interview techniques, which was chosen a priori. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines informed the review report. RESULTS: From an initial list of 789 citations, eight studies using CSR in research interviews were included in the review: six from North America, one from the Netherlands, and one from Ireland. The most common purpose of included studies was to examine the influence of guidelines on physicians' decisions. The number of interviewees ranged from seven to twenty nine, while the number of charts discussed per interview ranged from one to twelve. CSR gave insights into physicians' reasoning for actions taken or not taken; the unrecorded social and clinical influences on decisions; and discrepancies between physicians' real and perceived practice. Ethical concerns and the training and influence of the researcher were poorly discussed in most of the studies. Potential pitfalls included the risk of recall, selection and observation biases. CONCLUSIONS: Despite the proven validity, reliability and acceptability of CSR in assessment interviews in medical education, its use in clinical research is limited. Application of CSR in qualitative research brings interview data closer to the reality of practice. Although further development of the approach is required, we recommend a role for CSR in research interviews on decision-making in clinical practice.
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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 | 0.223 | 0.523 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.052 | 0.042 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.004 |
| 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".