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Record W2963890305 · doi:10.1111/nin.12313

Applying the concept of structural empowerment to interactions between families and home‐care nurses

2019· article· en· W2963890305 on OpenAlexafffundabout
Laura Funk, Kelli Stajduhar, Melissa Giesbrecht, Denise Cloutier, Allison Williams, Faye Wolse

Bibliographic record

VenueNursing Inquiry · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsMcMaster UniversityUniversity of VictoriaUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsEmpowermentContext (archaeology)NursingInterpretation (philosophy)Control (management)PsychologyHealth careMedicinePolitical science

Abstract

fetched live from OpenAlex

Interpretations of family carer empowerment in much nursing research, and in home-care practice and policy, rarely attend explicitly to families' choice or control about the nature, extent or length of their involvement, or control over the impact on their own health. In this article, structural empowerment is used as an analytic lens to examine home-care nurses' interactions with families in one Western Canadian region. Data were collected from 75 hrs of fieldwork in 59 interactions (18 nurses visiting 16 families) and interviews with 12 nurses and 11 family carers. Generally, nurses prioritized client empowerment, and their practice with families appeared oriented to supporting their role and needs as carers (i.e. rather than as unique individuals beyond the caring role), and reinforcing the caring role through validation and recognition. Although families generally expressed appreciation for these interactions, a structural empowerment lens illustrates how the broad context of home care shapes the interpretation and practice of empowerment in ways that can, paradoxically, be disempowering for families. Opportunities to effectively support family choice and control when a client is being cared for at home are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.456

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 studies0.0000.000
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 head0.082
GPT teacher head0.427
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2019
Admission routes3
Has abstractyes

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