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Record W130671821 · doi:10.1515/ijnes-2014-0073

Nursing Clinical Instructor Experiences of Empowerment in Rwanda: Applying Kanter’s and Spreitzer’s Theories

2016· article· en· W130671821 on OpenAlexaff
Mary Thuss, Yolanda Babenko‐Mould, Mary‐Anne Andrusyszyn, Heather K. Spence Laschinger

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2016
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsWestern University
Fundersnot available
KeywordsEmpowermentCompetence (human resources)NursingPsychologyQualitative researchNursing theoryMedical educationPedagogySociologyMedicineSocial psychologyMEDLINEPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of this study was to explore Rwandan nursing clinical instructors' (CIs) experiences of structural and psychological empowerment. CIs play a vital role in students' development by facilitating learning in health care practice environments. Quality nursing education hinges on the CI's ability to enact a professional role. A descriptive qualitative method was used to obtain an understanding of CIs empowerment experiences in practice settings. Kanter's Theory of Structural Power in Organizations and Spreitzer's Psychological Empowerment Theory were used as theoretical frameworks to interpret experiences. Interview data from 21 CIs were used to complete a secondary analysis. Most participants perceived the structural components of informal power, resources, and support while formal power and opportunity were limited, diminishing their sense of structural empowerment. Psychological empowerment for CIs stemmed from a sense of competence, meaning, impact and self-determination they had for their teaching roles and responsibilities in the practice setting.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.009
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.438
Teacher spread0.381 · 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 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

Citations16
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Journal of Nursing Education ScholarshipSame topicNursing education and managementFrench-language works237,207