Resident learning trajectories in the workplace: A self‐regulated learning analysis
Bibliographic record
Abstract
CONTEXT: Research in workplace learning has emphasised trainees' active role in their education. By focusing on how trainees fine-tune their strategic learning, theories of self-regulated learning (SRL) offer a unique lens to study workplace learning. To date, studies of SRL in the workplace tend to focus on listing the factors affecting learning, rather than on the specific mechanisms trainees use to regulate their goal-directed activities. To inform the design of workplace learning interventions that better support SRL, we asked: How do residents navigate their exposure to and experience performing invasive procedures in intensive care units? METHODS: In two academic hospitals, we conducted post-call debriefs with residents coming off shift and later sought their elaborated perspectives via semi-structured interviews. We used a constant comparative methodology to analyse the data, to iteratively refine data collection, and to inform abductive coding of the data, using SRL principles as sensitising concepts. RESULTS: We completed 29 debriefs and nine interviews with 24 trainees. Participants described specific mechanisms: identifying, creating, avoiding, missing and competing for opportunities to perform invasive procedures. While using these mechanisms to engage with procedures (or not), participants reported: distinguishing trajectories (i.e. becoming attuned to task-relevant factors), navigating trajectories (i.e. creating and interacting with opportunities to perform procedures), and co-constructing trajectories with their peers, supervisors and interprofessional team members. CONCLUSIONS: We identified specific SRL mechanisms trainees used to distinguish and navigate possible learning trajectories. We also confirmed previous findings, including that trainees become attuned to interactions between personal, behavioural and environmental factors (SRL theory), and that their resulting learning behaviours are constrained and guided by interactions with peers, supervisors and colleagues (workplace learning theory). Making learning trajectories explicit for clinician teachers may help them support trainees in prioritising certain trajectories, in progressing along each trajectory, and in co-constructing their plans for navigating them.
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".