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Record W2111904459 · doi:10.1111/nin.12026

The politics of knowledge: implications for understanding and addressing mental health and illness

2013· article· en· W2111904459 on OpenAlexaff
Emily Jenkins

Bibliographic record

VenueNursing Inquiry · 2013
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoliticsMental illnessExperiential knowledgeMental healthPublic relationsSociologySociology of health and illnessAction (physics)Engineering ethicsHealth carePsychologyPolitical scienceEpistemologyPsychiatryLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.490
GPT teacher head0.516
Teacher spread0.026 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2013
Admission routes1
Has abstractyes

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