The Importance of Cultivating a Preference for Complexity in Veterinarians for Effective Lifelong Learning
Bibliographic record
Abstract
Much attention has been paid to the link between students' approaches to study and the quality of their learning. Less attention has been paid to the lifelong learner. We conceptualized a tripartite relationship between three measures of learning preference: conceptions of knowledge (construction and use vs. intake), need for cognition (high vs. low), and approach to study (deep vs. surface) and hypothesized that an individual's profile on these three measures-reconceptualized as a preference for complexity versus simplicity-would affect their attitude toward continuing professional development (CPD). A questionnaire was mailed to 2,000 randomly selected, home-practicing UK veterinarians to quantify their learning preferences, motivation to engage in CPD, and perception of barriers to participation and to assess the relationships between these constructs. Analysis of 775 responses (a 38.8% response rate) confirmed our tripartite model of learning and showed that a preference for complexity was negatively correlated with barriers and positively correlated with intrinsic, social, and extrinsic motivating factors, suggesting that all play a role in the continuing education of this group of professionals. A preference for simplicity was negatively correlated with social motivation and positively correlated with barriers. This study demonstrates that approach not only affects the quality of learning but crucially affects motivation to engage in CPD and perception of barriers to lifelong learning. This should emphasize to veterinary educators the importance of fostering a preference for complexity from an early age, both in terms of its immediate benefits (better understanding) and longer-term benefits (continued engagement with learning).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".