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Transforming work place relationships through shared decision making

2010· article· en· W1513630577 on OpenAlexaff
Maura MacPhee, Andrea Wardrop, Cheryl Campbell

Bibliographic record

VenueJournal of Nursing Management · 2010
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWorkloadEmpowermentStaffingNursingFocus groupJob satisfactionNursing managementTeamworkPsychologyKnowledge managementMedicineBusinessManagementSocial psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

AIM: Using Donabedian's Structure-Process-Outcomes (SPO) paradigm, this study explored the SPO linkages related to nurse-nurse leader shared decision making around workload issues, such as safe staffing assignments. BACKGROUND: Shared decision making represents nurses' control over practice, which is associated with positive nurse outcomes, such as job satisfaction. This study is based upon four project sites where nurse-led project teams addressed workload issues. METHODS: Participatory action research was used, with the authors acting as participant observers. Four sites were case ordered and analysed: least successful to most successful outcomes. Cross-case matrices were constructed to identify SPO linkages. Data included observation field notes, interviews and focus groups. RESULTS: Operations leaders with formal access to empowerment structures, such as information and resources, were the critical link to successful outcomes. Sites with conflict that blocked team-operations leader relationships were unable to engage in effective, sustainable decision making. CONCLUSIONS: Effective work relationships among teams consisting of staff and front-line leaders contributed to successful outcomes, but team-operations leader relationships made the biggest difference. IMPLICATIONS FOR NURSING MANAGEMENT: Formal access to power through leadership is critical for building and sustaining processes that promote and sustain nurses' control over practice.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.352
Teacher spread0.301 · 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 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

Citations43
Published2010
Admission routes1
Has abstractyes

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