The Critical Thinking Skills of Practicing Family Physicians: A Population-Based Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Critical thinking (CT) skills are an important aspect of clinical reasoning and diagnosis. The goals of this study were to (1) examine levels of CT skills of practicing family physicians, (2) compare the CT skills of practicing family physicians to family medicine residents, and (3) identify individual variables and practice characteristics predictive of CT skills. . METHODS: We used a population-based, cross-sectional design to compare practicing and resident family physicians and examine the predictors of CT skills in practicing family physicians. Sixty-two practicing family physicians were recruited across Canada. We used data from 59 family medicine residents at a single institution in Canada. We used the California Critical Thinking Skills Test (CCTST) to measure CT skills. We analyzed data using descriptive and univariate analysis, multivariate analysis of variance, and hierarchical multiple linear regression. CT skills were further examined in follow-up analysis using polynomial regression. RESULTS: Residents performed better than practicing physicians on nearly all aspects of CT (P<.005). Age was the strongest predictor of CT skills in practicing physicians (P<.005); CT skills declined with age as a quadratic function (P<.005). CONCLUSIONS: As a group, practicing family physicians exhibited lower scores on the CCTST compared to family medicine residents. CT skills showed a decline with age, accelerating after approximately age 60 years. The results of the study have implications for continuing education and assessment of physicians' clinical skills. Further research is required to better understand what other predictors may be important for CT skills of practicing family physicians.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".