Preparation for future learning: a missing competency in health professions education?
Bibliographic record
Abstract
CONTEXT: Evidence suggests that clinicians may not be learning effectively from all facets of their practice, potentially because their training has not fully prepared them to do so. To address this gap, we argue that there is a need to identify systems of instruction and assessment that enhance clinicians' 'preparation for future learning'. Preparation for future learning (PFL) is understood to be the capacity to learn new information, to use resources effectively and innovatively, and to invent new strategies for learning and problem solving in practice. CURRENT STATE: Education researchers have developed study designs that use dynamic assessments to measure what trainees have acquired in the past, as well as what they are able to learn in the present. More recently, researchers have also started to emphasise and measure whether and how trainees take action to gain the information they need to learn. Knowing that there are study designs and emerging metrics for assessing PFL, the next question is how to design instruction that helps trainees develop PFL capacities. Although research evidence is still accumulating, the current evidence base suggests training that encourages 'productive failure' through guided discovery learning (i.e. where trainees solve problems and perform tasks without direct instruction, though often with some form of feedback) creates challenging conditions that enhance learning and equip trainees with PFL-related behaviours. CONCLUSIONS: Preparation for future learning and the associated capacity of being adaptive as one learns in and from training and clinical practice have been missed in most contemporary training and assessment systems. We propose a research agenda that (i) explores how real-world adaptive expert activity unfolds in the health care workplace to inform the design of instruction for developing PFL, (ii) identifies measures of behaviours that relate to PFL, and (iii) addresses potential sociocultural barriers that limit clinicians' opportunities to learn from their daily practice.
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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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".