20 Clinical competencies of the clinical nurse specialist in Care of the Older Person in a geriatric assessment unit
Bibliographic record
Abstract
Abstract Background Access to geriatric assessment, coordination between healthcare providers and integration of care across the health system are critical to meet the care needs of older adults living in the community with complex care needs. Clinical Nurse Specialists (CNS) with knowledge and clinical skills in gerontological nursing are key for the provision of care in this cohort. Our aim was to conduct an audit of CNS activity in the Geriatric Assessment Unit (GAU) in relation to specialist nursing clinical competencies. Methods We conducted a retrospective audit of CNS activity during the first quarter of 2023 with a focus on the CNS clinical core competencies as defined in the Framework for the Establishment of Clinical Nurse\Midwife Specialist Posts in two GAU in Louth. Results In total, n = 301 patients attended the two GAU in the first quarter of 2023, 58% female, with mean age 79.90 (95% CI 79.18–80.62), n = 78 of whom were new to the service. Within an interdisciplinary approach, a Comprehensive Geriatric Assessment (CGA) is conducted by the CNS, which includes obtention of a detailed medical history, nutritional assessment, falls risk, skin integrity, frailty and dependency level, assessment of continence, cognitive screening and swallow screening. These assessments led to n = 273 referrals to community-based services, including Public Health Nurse (n = 51), Occupational Therapist (n = 38) and Memory Rehab (n = 18). Mean waiting time to attend the clinic for the first time was 104.13 days (95% CI 89.79–118.47). Conclusion The CNS in Care of the Older Person conducts a CGA, identifies unmet care needs and plans, coordinates and initiates person-centred care for older adults living in the community with complex care needs. CNS input is key to achieve adequate management of complex care needs while avoiding negative outcomes and promoting quality of life.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".