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Record W2077630893 · doi:10.12927/cjnl.2012.23262

Process of Seeking Connectivity: Social Relations of Power between Staff Nurses and Nurse Managers

2012· article· en· W2077630893 on OpenAlexaffvenueabout
Sonia Udod

Bibliographic record

VenueNursing leadership · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEmpowermentNursingGeneral partnershipPower (physics)Context (archaeology)PsychologyNurse managerPerspective (graphical)Process (computing)BusinessMedicinePolitical science

Abstract

fetched live from OpenAlex

This study explored the process of how power is exercised in nurse-manager relationships in the hospital setting, to better understand what fosters and constrains staff nurse empowerment. Semi-structured interviews and participant observations were conducted with 26 participants in a hospital in Western Canada. Seeking connectivity was the basic social process in which nurses strive to connect with their manager to create a workable partnership in the provision of high-quality patient care while responding to the demands of the organizational context. The overarching finding was that the manager plays a critical role in modifying the work environment for nurses and, as such, nurses seek connection with their manager. Findings revealed two patterns within the process of seeking connectivity: (a) in the absence of a meaningful engagement with the manager, power was held over nurses through institutional patterns of behaviour and practices, and nurses employed a variety of resistance strategies; (b) when managers provided guidance and engaged nurses as co-collaborators, power was shared and nurses were able to influence patient outcomes positively. The results of this study support Laschinger's program of research on nurse empowerment from an organizational perspective, and advance nurse empowerment from a critical perspective.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.303
GPT teacher head0.495
Teacher spread0.192 · 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

Citations8
Published2012
Admission routes3
Has abstractyes

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