Bibliographic record
Abstract
Conceptualizations of workplace learning have moved from knowledge acquisition to learning as participation in the practices and cultures of the workplace environment. Along with this has come an appreciation of applicability of sociocultural learning theories, which frame learning as occurring within "communities of practice" or learning being "situated" within a workplace environment where collaboration and social interaction are fundamental to the learning process. These conceptualizations of workplace learning are ideally suited to health professions where learners are supervised in clinical work environments and then continue to work in team-based environments as graduates. However, what happens to workplace learning for novice practitioners who have limited periods of clinical supervision and then graduate into solo or small group practices (which may also be in rural or remote locations) and embark on long working careers without supervision? This paper argues workplace learning needs to be scaffolded and supported to reach its full potential in these environments. Drawing on workplace-based learning theory, we highlight the ubiquitous nature of learning in the workplace, the importance of active engagement, reflection, and individual meaning making. Through this reframing of traditional notions of continuing professional development, we emphasize the importance of patients, students, and other practitioners as partners in workplace learning for solo practitioners. We also focus on the role of educators, professional associations, and regulators in helping solo practitioners recognize, access, and maximize the learning opportunities inherent in relatively isolated practice environments.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".