The quality of questions and use of resources in self-directed learning: Personal learning projects in the maintenance of certification
Bibliographic record
Abstract
INTRODUCTION: To engage effectively and efficiently in self-directed learning and knowledge-seeking practices, it is important that physicians construct well-formulated questions; yet, little is known about the quality of good questions and their relationship to self-directed learning or to change in practice behavior. METHODS: Personal learning projects (PLPs) submitted to the Canadian Maintenance of Certification program were examined to include underlying characteristics, quality of therapeutic questions (population, intervention, comparator, outcome [PICO] mnemonic), and relationships between stage of change and level of evidence used to resolve questions. RESULTS: We assessed 1989 submissions (from 559 Fellows of the Royal College of Physicians and Surgeons of Canada [RCPSC]). The majority of submissions were by males (69.2%) aged 40-59 (59.4%) with an average of 24.3 (range 6-58, SD 11.1) years since graduation. The most frequent submissions were treatment (36.6%) and diagnosis (22.3%) questions. Half of all questions described > or =2 components (PICO), and only 3.7% of questions included all 4 components. Cross tabulations indicated only 1 significant trend for the use of narrative reviews and the outcome "integrating new knowledge' (P < .000). DISCUSSION: Self-directed learning skills comprise an important strategy for specialists maintaining or expanding their expertise in patient care, but an important obstacle to answering patient care questions is the ability to formulate good ones. Engagement in most major learning activities is stimulated by management of a single patient: formal accredited group learning events are of limited value in starting episodes of self-directed learning. Low levels of evidence used to address learning projects. Future research should determine how best to improve the quality of questions submitted and whether or not these changes result in increased efficiencies, more appropriate uses of evidence, and increased changes in practice behaviors.
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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 | 0.058 | 0.231 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".