Microlearning to improve <scp>CPD</scp> learning objectives
Bibliographic record
Abstract
BACKGROUND: Despite active involvement in teaching, clinical educators facilitating the continuing professional development (CPD) of their fellow specialists may not have formal training in medical education. Although required to write focused, measurable, topic-relevant, attainable and time-bound learning objectives to clearly inform learners on their learning intentions, CPD educators often receive no training on how to develop them. Microlearning is an online learning format occurring without real-time or interpersonal interaction, aiming to deliver easily accessible small units of focused information that are readily applicable for professionals. We hypothesised that Portuguese ophthalmologist educators lecturing to their fellow specialists would benefit from a microlearning experience (MLE) to improve the quality of their learning objectives. METHODS: We created an MLE about writing effective learning objectives. In phase 1, 25 clinical educators, scheduled to lecture at an ophthalmology conference in Portugal, were invited to watch the MLE, write and classify their learning objectives according to Bloom's modified taxonomy, and complete an evaluation survey. In phase 2, 86 clinical educators were invited to view the MLE and complete the survey. RESULTS: In phase 1, 20% of participants completed the exercise and survey. They categorised their objectives high on Bloom's taxonomy, considered the MLE useful and stated their intent to apply the principles learned in practice. In phase 2, 29% of participants provided feedback. All agreed that the intervention was clear and useful and 87% expressed an intent to use this information in their educational practice. CONCLUSIONS: The majority of participants found the MLE clear and useful. Further studies are necessary to measure the impact of the MLEs used by clinical educators.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".