Impact of Educational Activity Formats, Online or In-Person, on the Intention of Medical Specialists to Adopt a Clinical Behaviour: A Comparative Study
Bibliographic record
Abstract
COVID-19 accelerated continuing professional development (CPD) delivered online. We aimed to compare the impact of in-person versus online CPD courses on medical specialists’ behavioural intentions and subsequent behaviour. In this comparative before-and-after study, medical specialists attended in-person courses on nine clinical topics. A second group attended an adapted online version of these courses. Behavioural intention and its psychosocial determinants were measured before and immediately after the courses. Behaviour change was measured six months later. Generalised estimating equation (GEE) models were used to compare the impact of course formats. A total of 82/206 in-person registrants (mean age: 52±10 years; 50% men) and 318/506 on-line registrants (mean age: 49±12 years; men: 63%) participated. Mean intention before in-person courses was 5.99±1.31 and 6.43±0.80 afterwards (average intention gain 0.44, CI: 0.16–0.74; p=0.003); mean intention before online courses was 5.53±1.62 and 5.98±1.40 afterwards (average intention gain of 0.45, CI: 0.30–0.58; p<0.0001). Difference in intention gain between groups was not statistically significant. Behaviour reported six months later was not significantly associated with post-course intention in either group. However, the intention difference increased significantly among those who said they had adopted the targeted behaviour (paired wilcoxon test: n = 40 and p-value=0.002) while it did not increase significantly in the group of those who had not adopted a targeted behaviour (paired wilcoxon test: n = 16 and p-value=0.223).In conclusion, the increase in intention of specialists after CPD courses was similar whether the course was in-person or online. Also, an increase in intention in both groups signalled more likelihood of adoption.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".