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A Gender Perspective on Conflict Management Strategies of Nurses

2001· review· en· W2053088625 on OpenAlexaff
Patricia E.B. Valentine

Bibliographic record

VenueJournal of Nursing Scholarship · 2001
Typereview
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPerspective (graphical)Construct (python library)Conflict managementPsychologyNursing managementNursingMedicineSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

PURPOSE: To apply a gender perspective to synthesis of research findings on conflict management. ORGANIZING CONSTRUCT: The Thomas-Kilmann Mode Instrument (TKI), for measuring five conflict-handling strategies: avoiding, compromising, collaborating, accommodating, and competing. METHOD: Nursing research studies with the TKI and other studies are synthesized from perspectives in three gender theories. CONCLUSIONS: Findings were that two conflict management strategies, avoiding and compromising, were used predominantly by all categories of nurses. Possible reasons for over- and underuse of the remaining three strategies (collaborating, accommodating, competing) are described. Implications of these findings for nurses and nursing organizations 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 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)0.005
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.004
Scholarly communication0.0020.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.255
GPT teacher head0.493
Teacher spread0.239 · 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
GenreReview

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

Citations83
Published2001
Admission routes1
Has abstractyes

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