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Record W1533738879

Family health nursing and empowering relationships.

2006· article· en· W1533738879 on OpenAlexaff
Megan Aston, Donna Meagher‐Stewart, Debbie Sheppard-LeMoine, Adele Vukic, Andrea Chircop

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEmpowermentNursingIdeologyPublic health nursingPsychologyPower (physics)Agency (philosophy)Normalization (sociology)PerceptionPublic healthSociologyPedagogyMedicinePolitical sciencePoliticsSocial science
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: To examine how empowerment, as an ideology and a practice of teaching and learning, was understood and applied by public health nurses (PHNs) in health education with child bearing and child rearing families. METHOD: Feminist poststructuralism was used to guide data collection and analysis. In-depth, individual interviews were conducted with three mothers and three PHNs and explored the different perspectives held by mothers and PHNs during a home visit. FINDINGS: Moments of conflict, contradiction, affirmation, and agreement highlighted various empowering relations. Individual choice and recognition of knowledge and power exemplified how both mothers and PHNs used their "agency" to position themselves into a particular relationship. The analysis includes five sections: (a) mother's perceptions of PHNs, (b) normalization as problematic: the good/bad dichotomy, (c) professional/expert: the balance of power, (d) working the relationship, and (e) reflections on empowerment. CONCLUSION: The information gathered from this studyprovides a rich understanding of the nurses' educational practices with new mothers.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.005
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.141
GPT teacher head0.373
Teacher spread0.232 · 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 designNot applicable
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

Citations37
Published2006
Admission routes1
Has abstractyes

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