Learning and professional acculturation through work: Examining the clinical learning environment through the sociocultural lens
Bibliographic record
Abstract
Purpose: We examined studies of the clinical learning environment from the fields of sociology and organizational culture to (i) offer insight into how workplace culture has informed research on postgraduate trainee learning and professional development; (ii) highlight limitations of the literature; and (iii) suggest practical ways to apply sociocultural concepts to challenges in the learning environment.Materials and methods: Concepts were explored by participants at a consensus conference in October 2018.Results: We identified three enduring foci for research using a sociocultural lens: the hidden curriculum, exploration of medical errors, and the impact of time pressures on the relational nature of clinical education. Limitations included the lower value attributed to informal learning and a pejorative valuation of the hidden curriculum; and disconnect between practices in clinical settings and the priorities of the larger organization.Conclusions: Research on the learning environment using a sociocultural lens suggest workplace goals, norms and practices determined which learners engage in learning-relevant activities, to what extent, and the degree of guidance provided, with these factors creating “tacit” curricula that may support or compete with formal learning goals. We close with guidance on how sociocultural constructs could inform research to improve the learning environment.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".