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Record W1917036387 · doi:10.12927/cjnl.2015.24352

Teamwork and Patient Care Teams in an Acute Care Hospital

2015· article· en· W1917036387 on OpenAlexaffvenueabout
Andrea Rochon, Roberta Heale, Elena Hunt, Michele Lucie Parent

Bibliographic record

VenueNursing leadership · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsLaurentian UniversityProvidence Health Care
Fundersnot available
KeywordsTeamworkNursingStaffingAcute carePerceptionNursing carePatient carePsychologyNursing staffMedicineHealth careManagementPolitical science

Abstract

fetched live from OpenAlex

The literature suggests that effective teamwork among patient care teams can positively impact work environment, job satisfaction and quality of patient care. The purpose of this study was to determine the perceived level of nursing teamwork by registered nurses, registered practical nurses, personal support workers and unit clerks working on patient care teams in one acute care hospital in northern Ontario, Canada, and to determine if a relationship exists between the staff scores on the Nursing Teamwork Survey (NTS) and participant perception of adequate staffing. Using a descriptive cross-sectional research design, 600 staff members were invited to complete the NTS and a 33% response rate was achieved (N=200). The participants from the critical care unit reported the highest scores on the NTS, whereas participants from the inpatient surgical (IPS) unit reported the lowest scores. Participants from the IPS unit also reported having less experience, being younger, having less satisfaction in their current position and having a higher intention to leave. A high rate of intention to leave in the next year was found among all participants. No statistically significant correlation was found between overall scores on the NTS and the perception of adequate staffing. Strategies to increase teamwork, such as staff education, among patient care teams may positively influence job satisfaction and patient care on patient care units.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.123
GPT teacher head0.433
Teacher spread0.310 · 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 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

Citations9
Published2015
Admission routes3
Has abstractyes

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