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Record W2999768661 · doi:10.21037/apm.2019.12.06

Analysis of daily goal sheets on physician-nurse collaboration attitude

2020· article· en· W2999768661 on OpenAlexaff
Xin Qian, Li Jun Li, Yi Yu Zhuang, Yu Hong, Zhong Heng Zhang, Li Feng Xing, Ning Liu, Hong Chao Li, Ru Jin Zhang, Fu‐Chih Lai, Charles B. Simone, Edward Chow

Bibliographic record

VenueAnnals of Palliative Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineNursingIntervention (counseling)Intensive care unitNurse practitionersScale (ratio)Perspective (graphical)Health careEmergency departmentFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Optimal communication and collaboration between inter-disciplinary health care providers is critical to ensuring high quality patient care. We aimed to quantify the impact on physician-nurse collaboration (PNC) of implementing daily goal sheets (DGSs) in emergency settings. METHODS: The usage of a DGS was administered in morning rounds in an emergency intensive care unit (ICU) for four consecutive months. A Jefferson Scale of Attitudes Toward Physician-Nurse Collaboration (JSAPNC) form was used before (n=113) and after (n=107) the intervention to evaluate the attitudes of PNCs from the perspective of both physicians and nurses. RESULTS: There is a significant positive relation between the attitude to PNC and the participant age, educational background, and professional rank and title before DGS application (P<0.01 for each), whereas there was no significant difference observed after the initiation of the DGS. CONCLUSIONS: The use of a DGS improves physician-nurse collaborations in emergency care settings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.519
Teacher spread0.395 · 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 designObservational
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

Citations6
Published2020
Admission routes1
Has abstractyes

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