A concept analysis of abductive reasoning
Bibliographic record
Abstract
AIM: To describe an analysis of the concept of abductive reasoning. BACKGROUND: In the discipline of nursing, abductive reasoning has received only philosophical attention and remains a vague concept. In addition to deductive and inductive reasoning, abductive reasoning is not recognized even in prominent nursing knowledge development literature. Therefore, what abductive reasoning is and how it can inform nursing practice and education was explored. DESIGN: Concept analysis. DATA SOURCES: Combinations of specific keywords were searched in Web of Science, CINAHL, PsychINFO, PubMed, Medline and EMBASE. The analysis was conducted in June 2012 and only literature before this period was included. No time limits were set. METHODS: Rodger's evolutionary method for conducting concept analysis was used. RESULTS: Twelve records were included in the analysis. The most common surrogate term was retroduction, whereas related terms included intuition and pattern and similarity recognition. Antecedents consisted of a complex, puzzling situation and a clinician with creativity, experience and knowledge. Consequences included the formation of broad hypotheses that enhance understanding of care situations. Overall, abductive reasoning was described as the process of hypothesis or theory generation and evaluation. It was also viewed as inference to the best explanation. CONCLUSION: As a new approach, abductive reasoning could enhance reasoning abilities of novice clinicians. It can not only incorporate various ways of knowing but also its holistic approach to learning appears to be promising in problem-based learning. As nursing literature on abductive reasoning is predominantly philosophical, practical consequences of abductive reasoning warrant further research.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".