(Mis)perceptions of Continuing Education: Insights From Knowledge Translation, Quality Improvement, and Patient Safety Leaders
Bibliographic record
Abstract
INTRODUCTION: Minimal attention has been given to the intersection and potential collaboration among the domains of continuing education (CE), knowledge translation (KT), quality improvement (QI), and patient safety (PS), despite their overlapping objectives. A study was undertaken to examine leaders' perspectives of these 4 domains and their relationships to each other. In this article, we report on a subset of the data that focuses on how leaders in KT, PS, and QI define and view the domain of CE and opportunities for collaboration. METHODS: This study is based on a qualitative interpretivist framework to guide the collection and analysis of data in semistructured interviews. Criterion-based, maximum variation, and snowball sampling were used to identify key opinion leaders in each domain. The sample consisted of 15 individuals from the domains KT, QI, and PS. The transcripts were coded using a directed content analysis approach. RESULTS: The findings are organized into 3 thematic subsections: (1) definition and interpretation of CE, (2) concerns about relevance and effectiveness of CE, and (3) opportunities for collaboration among CE and the other domains. While there were slight differences among the data from the leaders of each domain, common themes were generally reported. DISCUSSION: The findings provide CE leaders with information about KT, QI, and PS leaders' (mis)perceptions about CE that can inform future strategic planning and activities. CE leaders can play an important role in building upon initial collaborations among the domains to enable their strengths to complement each other.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".