Does discourse matter? Using critical inquiry to engage in knowledge development for practice
Bibliographic record
Abstract
Recent years have seen an increase in critical analyses of discourses of policy and practice. However, some argue that this form of scholarship is not central to understanding the concerns of day-to-day practice in the health care context. We propose the converse and contend that critical analyses have particularly important contributions to make because they challenge us to examine what are largely taken for granted aspects of practice. One context in which such examinations have been instructive is primary healthcare. This article is intended to further the dialogue on the ways the culture concept is taken up in health care. We use the case of culture and health to illustrate the ways discourses are taken up in local and official contexts and to demonstrate how different discourses and related institutional practices, shape individuals' relationships with others in the community context.
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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 | 0.157 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.018 | 0.133 |
| Scholarly communication | 0.042 | 0.055 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".