Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The purpose of this article was to describe the collaborative model established between the specialized nurse practitioner (SNP) and the clinical nurse specialist (CNS), within the cardiac surgery population. BACKGROUND/RATIONALE: The literature has shown a trend that SNPs and CNSs work in parallel or even in competition. Little has been written about a collaborative model and the advantages to working together toward a mutual goal. It was felt that a collaborative model between advance practice nurses could demonstrate the advantages of using their unique perspectives to achieve improvement in the quality of care given to the patients. DESCRIPTION OF THE PROJECT/INNOVATION: The Structuration Model of Interprofessional Collaboration was used as a guide to our interventions and elaboration of our project. The team set out to establish, promote, and nurture a collaborative approach to care for the cardiac surgery population from admission to 3 months after discharge. OUTCOMES: A collaborative relationship was achieved between the SNP and the CNS, to the benefit of the patient population. INTERPRETATION/CONCLUSION: Collaboration between these 2 advanced practice nurses is realistic and attainable. The advantages to this relationship outweigh the negative feelings harbored toward each professional title. By working in collaboration instead of in parallel, resources are pooled together to achieve greater services and care for the patients. IMPLICATIONS: A change in attitude between professionals has a positive impact on working relationships, partnerships, and communication. Ultimately, it is an advantage for the advanced practice nurse, the multidisciplinary team, and the patient.
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".