Mapping the dark matter of context: a conceptual scoping review
Bibliographic record
Abstract
CONTEXT: Like dark matter, the contexts for medical education are largely invisible to those within them, although context can have profound influences on teaching, learning and practice. For something that is so intrinsic to the field of medical education, the concept of context remains troubling to scholars and those running medical education programmes. This paper reports on a critical and conceptual review of the concept of context within the medical education literature and beyond. METHODS: A review was undertaken drawing on two sources: concepts of context in the medical education literature, and concepts of context across multiple academic disciplines. This body of material was iteratively, discursively and inductively synthesised. RESULTS: Few of the articles from the medical education literature described or defined context directly, tending instead to focus on describing specific elements of context, such as clinical disciplines, physical settings and political pressures, that could or did influence learning outcomes. The results were framed in terms of what context 'is', how context works (in terms of context-mechanism-outcome), and how context can be represented using patterns. The authors propose a definition of context in medical education, along with the means to model, contrast and compare different contexts based on recurring patterns. CONCLUSIONS: Context matters in medical education and it can, despite many challenges, be considered systematically and objectively. The findings from this study both represent a catalyst and challenge medical education researchers to look at context afresh.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.042 | 0.036 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| 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 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".