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Record W2317765043 · doi:10.1097/jtn.0000000000000090

Advanced Practice Nursing, Health Care Teams, and Perceptions of Team Effectiveness

2014· review· en· W2317765043 on OpenAlexaff
Kelley Kilpatrick, Mélanie Lavoie‐Tremblay, Judith A. Ritchie, Lise Lamothe

Bibliographic record

VenueJournal of Trauma Nursing · 2014
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité du Québec en OutaouaisUniversité du Québec à Montréal
Fundersnot available
KeywordsTeamworkTeam effectivenessHealth careContext (archaeology)NursingSituatedPsychologyProcess (computing)Team compositionKnowledge managementMedicinePolitical science

Abstract

fetched live from OpenAlex

This article summarizes the results of an extensive review of the organizational and health care literature of advanced practice nursing (APN) roles, health care teams, and perceptions of team effectiveness. Teams have a long history in health care. Managers play an important role in mobilizing resources, guiding expectations of APN roles in teams and within organizations, and facilitating team process. Researchers have identified a number of advantages to the addition of APN roles in health care teams. The process within health care teams are dynamic and responsive to their surrounding environment. It appears that teams and perceptions of team effectiveness need to be understood in the broader context in which the teams are situated. Key team process are identified for team members to perceive their team as effective. The concepts of teamwork, perceptions of team effectiveness, and the introduction of APN roles in teams have been studied disparately. An exploration of the links between these concepts may further our understanding the health care team's perceptions of team effectiveness when APN roles are introduced. Such knowledge could contribute to the effective deployment of APN roles in health care teams and improve the delivery of health care services to patients and families.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.549
Teacher spread0.516 · 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 designOther design
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

Citations13
Published2014
Admission routes1
Has abstractyes

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