Microlearning to improve <scp>CPD</scp> learning objectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it