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Record W2064101853 · doi:10.1177/1054773815577577

Exploring the Nurse Practitioner Role in Managing Fractures in Long-Term Care

2015· article· en· W2064101853 on OpenAlexafffund
Sharon Kaasalainen, Αλεξάνδρα Παπαϊωάννου, Jennifer Burgess, Mary Lou van der Horst

Bibliographic record

VenueClinical Nursing Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsConestoga CollegeMcMaster University
FundersCanadian Institutes of Health Research
KeywordsLong-term careNursingMedicineQualitative researchFamily medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the current level of involvement of nurse practitioners (NPs) in activities related to preventing and managing fractures in long-term care (LTC). This study used a sequential explanatory mixed methods design that included two phases-a cross-sectional survey followed by qualitative interviews. A final sample of 12 NPs completed the online survey for a response rate of 67%. Eleven of the 12 NPs who completed the survey agreed to participate in a follow-up interview. NPs reported that they were quite engaged in managing fractures in LTC; specifically, they were most active in caring for residents post-fracture. NPs described their role as being holistic in nature in their assessment and treatments related to managing fractures. The findings from this mixed method study add to the growing body of knowledge related to how NPs manage fractures in LTC.

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)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.446
GPT teacher head0.637
Teacher spread0.191 · 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 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
Published2015
Admission routes2
Has abstractyes

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