Paramedic Learning Style Preferences and Continuing Medical Education Activities: A Cross-Sectional Survey Study.
Bibliographic record
Abstract
BACKGROUND: Paramedics participate in continuing medical education (CME) to maintain their skills and knowledge. An understanding of learning styles is important for education to be effective. This study examined the preferred learning styles of ground ambulance paramedics and describes how their preferred learning styles relate to the elective CME activities these paramedics attend. METHODS: All paramedics (n=1,036) employed in a provincial ground ambulance service were invited to participate in a survey containing three parts: demographics, learning style assessed by the Kolb Learning Style Inventory (LSI), and elective CME activity. RESULTS: 260 paramedics (25%) participated in the survey. Preferred learning styles were: assimilator, 28%; diverger, 25%; converger, 24%; and accommodator, 23%. Advanced life support (ALS) providers had a higher proportion of assimilators (36%), and basic life support (BLS) providers had a higher proportion of divergers (30%). The learning style categories of CME activities attended by paramedics were: assimilators, 25%; divergers, 26%; convergers, 25%; and accommodators, 24%. CONCLUSION: These results suggest that paramedics are a diverse group of learners, and learning style differs within their demographics. Paramedics attend CME activities that complement all learning styles. Organizations providing education opportunities to paramedics should consider paramedics a diverse learning group when designing their CME programs.
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 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 | 0.000 | 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) | 0.002 | 0.001 |
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".