Virtual OSCE delivery: The way of the future?
Bibliographic record
Abstract
ADAPTATIONS strategies, and (d) regular follow-up 30-minute meetings between academic coaches and students are scheduled for accountability, reflection and feedback.Specifically, the academic coaching component of the programme emphasises: (a) self-reflection; (b) specific, measurable, achievable, relevant and time-based (SMART) goal setting; (c) the development of comprehensive study plans with deliberate use of effective learning strategies including spaced retrieval practice and elaboration, and (d) self-care. | WHAT LE SSON S WERE LE ARNED?The COVID-19 pandemic led to the digital transformation of teaching activities and demanded adaptive approaches in education.Our programme provided a strong support network for students during this challenging time.Teaching faculty members supported the streamlined, collaborative approach.Academic coaches offered timely oversight and early identification of students requiring support.The two-pronged approach allowed us to evaluate students holistically and to provide a customised academic support plan.From an implementation perspective, we recognised that although many components are described in MAL, focusing on key elements of the framework was enough.Students in the programme favoured the proactive support.Many acknowledged that individualised goal-directed study plans and follow-up meetings kept them accountable, reflective and motivated, and guarded against the use of ineffective learning strategies.Over time, some students invariably experienced declining motivation and difficulties in adhering to a study plan.At such times, it was important for coaches to help students reprioritise, troubleshoot and re-work study plans, as well as encouraging a deliberate holistic approach that included self-care and the maintenance of mental wellness.Importantly, we observed noticeable improvements in students' overall academic performance and we started to see increasing numbers of students proactively utilising programme resources to optimise their individual learning journeys.Further studies on academic impact and adaptive behaviour are needed as part of programme evaluation.We posit that regular and short-interval engagement with students during social isolation circumstances allows them to feel safe in reaching out for help.This model of academic coaching informed by theory supports students and empowers them with the skills necessary for effective learning, adapting and thriving in a health care environment challenged by uncertainty and ambiguity.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".