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Record W4323817472 · doi:10.1016/j.ijotn.2023.101015

Staff perspectives of a nurse practitioner-led orthogeriatric model of care in a large academic hospital: A mixed methods study

2023· article· en· W4323817472 on OpenAlexaffabout
Abby Ayoub, Inès Zombre, Chantal Backman, Chabot Chantal, Daniel Bédard, John Joanisse

Bibliographic record

VenueInternational Journal of Orthopaedic and Trauma Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsInstitut du Savoir MontfortUniversity of OttawaMontfort Hospital
Fundersnot available
KeywordsMedicineNursingContext (archaeology)Focus groupEconomic shortageHealth careOrthopedic surgeryGeriatric careHip fractureFamily medicineOsteoporosisSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Research has shown that models of care involving geriatric care in orthopedics decrease hospitalizations, mortality, length of stay and post-operative complications. This article presents an example of a nurse practitioner-led orthogeriatric model of care in a large academic hospital in Ontario. The overall goal was to explore staff perspectives regarding the nurse practitioner-led orthogeriatric model of care. METHODS: We conducted a mixed methods approach consisting of an online questionnaire, semi-structured interviews, and a focus group with staff. RESULTS: Questionnaire of staff showed overall support for functions of the NP within the model. Interviews with healthcare providers, and leadership as well as one focus group with orthopedic surgeons showed that despite the lack of formal awareness of the NP-led orthogeriatric model of care, staff felt that the model provided better care for the geriatric hip fracture population. CONCLUSION: In the current context of geriatricians' shortages to provide post-surgical care to geriatric patients, the staff described that geriatric care of hip fracture patients can be well accomplished by a NP. Further improvement efforts to create better awareness of the NP-led orthogeriatric model among the care team is needed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.388
Teacher spread0.371 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Orthopaedic and Trauma NursingSame topicHip and Femur FracturesFrench-language works237,207