Examining Associations Between Physician Data Utilization for Practice Improvement and Lifelong Learning
Bibliographic record
Abstract
INTRODUCTION: Practice data can inform the selection of educational strategies; however, it is not widely used, even when available. This study's purpose was to determine factors that influence physician engagement with practice data to advance competence and drive practice change. METHODS: A practice-based, pan-Canadian survey was administered to three physician subspecialties: psychiatrists (Psy), radiation oncologists (RO), and general surgeons (GS). The survey was distributed through national specialty society membership lists. The survey assessed factors that influence the use of data for practice improvement and orientation to lifelong learning, using the Jefferson Scale of Physician Lifelong Learning (JeffSPLL). Linear regression was used to model the relationship between the outcome variable frequency of data use and independent predictors of continuous learning to improving practice. RESULTS: A total of 305 practicing physicians (Psy = 203, RO = 53, GS = 49) participated in this study. Most respondents used data for practice improvement (n = 177, 61.7%; Psy = 115, 40.1%; RO = 35; 12.2%; GS = 27, 9.4%) and had high orientation to lifelong learning (JeffSPLL mean scores: Psy = 47.4; RO = 43.5; GS = 45.1; Max = 56). Linear regression analysis identified significant predictors of data use in practice being: frequency of assessing learning needs, helpfulness of data to improve practice, and frequency to develop learning plans. Together, these predictors explained 42.9% of the variance in physicians' orientation toward integrating accessible data into practice (R = 0.426, P < .001). DISCUSSION: This study demonstrates an association between practice data use and perceived data utility, reflection on learning needs and learning plan development. Implications for this work include process development for data-informed action planning for practice improvement for 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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".