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Record W2898894253 · doi:10.1097/ans.0000000000000221

Power and Resistance

2018· article· en· W2898894253 on OpenAlexaff
Allie Slemon, Vicky Bungay, Emily Jenkins, Helen Brown

Bibliographic record

VenueAdvances in Nursing Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of British ColumbiaNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsEmpowermentNarrativeResistance (ecology)Mental healthMicropowerPower (physics)NursingPsychologyPopulationPedagogyMedicinePsychotherapistPolitical science

Abstract

fetched live from OpenAlex

Mental health challenges are a leading health issue, and while nurses should be well positioned to provide care to this client population, nurses are not adequately prepared for this role during their education. This qualitative narrative inquiry explored nursing students' (N = 15) experiences within their mental health practicums. Analyzed through Foucault's concept of micropower, participants' narratives illustrate that students observed nurses holding power over patients, and enacted resistance to this power in a complex interplay of empowerment and disempowerment. Study findings speak to the need for systemic shifts in mental health practice and ongoing educator support of students in mental health practicums.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.092
Scholarly communication0.0140.012
Open science0.0010.015
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.013
GPT teacher head0.426
Teacher spread0.413 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations14
Published2018
Admission routes1
Has abstractyes

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