Bibliographic record
Abstract
CONTEXT: Health care research generally, and medical education research specifically, make increasingly sophisticated use of social science methods, but these methods are often detached from the theories that are the substantive core of the social sciences. Enhanced understanding of theory is especially valuable for gaining a broader perspective on how issues in medical education reflect the social processes that contextualise them. METHODS: This article reviews five social science theories, emphasising their relevance to medical education, beginning with the emergence of the sociology of health and illness in the 1950s, with Talcott Parsons' concept of the 'sick role'. Four turning points since Parsons are then discussed with reference to the theory developed by, respectively, Harold Garfinkel, Michel Foucault and Pierre Bourdieu, and what is called the 'narrative or dialogical turn'. In considering these, the author argues for a theory-grounded research that relates specific problems to what Max Weber called the 'fate of our times'. CONCLUSIONS: The conclusion considers how medical education research can critique the reproduction of a discourse of scarcity in health care, rather than participating in this discourse and legitimating the disciplinary techniques that it renders self-evident.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".