Bibliographic record
Abstract
BACKGROUND: Research in clinical reasoning has been conducted for over 30 years. Throughout this time there have been a number of identifiable trends in methodology and theory. PURPOSE: This paper identifies three broad research traditions, ordered chronologically, are: (a) attempts to understand reasoning as a general skill--the "clinical reasoning" process; (b) research based on probes of memory--reasoning related to the amount of knowledge and memory; and (c) research related to different kinds of mental representations--semantic qualifiers, scripts, schemas and exemplars. RESULTS AND CONCLUSIONS: Several broad themes emerge from this review. First, there is little evidence that reasoning can be characterised in terms of general process variables. Secondly, it is evident that expertise is associated, not with a single basic representation but with multiple coordinated representations in memory, from causal mechanisms to prior examples. Different representations may be utilised in different circumstances, but little is known about the characteristics of a particular situation that led to a change in strategy. IMPLICATIONS: It becomes evident that expertise lies in the availability of multiple representations of knowledge. Perhaps the most critical aspect of learning is not the acquisition of a particular strategy or skill, nor is it the availability of a particular kind of knowledge. Rather, the critical element may be deliberate practice with multiple examples which, on the hand, facilitates the availability of concepts and conceptual knowledge (i.e. transfer) and, on the other hand, adds to a storehouse of already solved problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".