Creativity research in medicine and nursing: A scoping review
Bibliographic record
Abstract
BACKGROUND: Creativity fuels societal progress and innovation, particularly in the field of medicine. The scientific study of creativity in medicine is critical to understanding how creativity contributes to medical practice, processes, and outcomes. An appraisal of the current scientific literature on the topic, and its gaps, will expand our understanding of how creativity and medicine interact, and guide future research. OBJECTIVES: We aimed to assess the quantity, trends, distribution, and methodological features of the peer-reviewed on creativity in medicine. METHODS: We searched the MEDLINE, EMBASE, and PsycINFO databases for peer-reviewed primary research publications on creativity in medicine. Screening, full-text review, and data extraction were performed independently and in duplicate by pairs of reviewers, with discrepancies resolved by a third reviewer. We performed descriptive analyses, graphically displaying the data using charts and maps where appropriate. RESULTS: Eighty-one studies were eligible for review, enrolling a total of 18,221 physicians, nurses and midwifes across all studies. Most research on creativity in medicine was published in the last decade, predominately in the field of nursing (75%). Researchers from Taiwan (22%) and the United States (21%) produced the most eligible publications, and the majority research was cross-sectional in nature (54%). There was substantial variability in the definitions of creativity adopted, and most studies failed to specify a definition of creativity. Forty-five different measurement tools were used to assess creativity, the most popular being divergent thinking tests such as the Torrance Test of Creative Thinking (24%) and Guilford Creativity Tests (16%). CONCLUSIONS: Peer-reviewed scientific research on creativity in medicine, mostly conducted in the nursing profession, is sparse and performed on variable methodological grounds. Further scientific research on the topic, as well as the development of medicine-specific definitions and measurement tools, is required to uncover the utility of creativity in the medical domain.
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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 | 0.042 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.044 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".