The politics of knowledge: implications for understanding and addressing mental health and illness
Bibliographic record
Abstract
While knowledge represents a valuable commodity, not all forms of knowledge are afforded equal status. The politics of knowledge, which entails the privileging of particular ways of knowing through linkages between the producers of knowledge and other bearers of authority or influence, represents a powerful force driving knowledge development. Within the health research and practice community, biomedical knowledge (i.e. knowledge pertaining to the biological factors influencing health) has been afforded a privileged position, shaping the health research and practice community's view of health, illness and appropriate intervention. The aim of this study is to spark critical reflection and dialogue surrounding the ways in which the politics of knowledge have constrained progress in addressing mental health and illness, one of today's leading public health issues. I argue that the hegemony of biological knowledge represents an ethical issue as it limits the breadth of knowledge available to support practitioners to 'do good' in terms of addressing mental illness. Given the power and influence inherent within the nursing community, I propose that nurses ought to engage in critical reflection and action in an effort to better situate the health research and practice community to effectively address the mental health of populations.
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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 | 0.045 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.129 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".