Staff perspectives of a nurse practitioner-led orthogeriatric model of care in a large academic hospital: A mixed methods study
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".