Bibliographic record
Abstract
With the wide range of clinical skills and responsibilities that home care nurses (HCNs) are expected to perform, it is important they are supported with access to relevant continuing nursing education (CNE) to perform their job safely and effectively. An integrative literature review was conducted to explore the current evidence on CNE for HCNs. Medline and CINAHL were searched and 13 articles that met the criteria were reviewed. The analysis identified three themes: (1) learning strategies (simulation, virtual gaming, elearning, traditional learning); (2) challenges (staffing, time, access, skill) and opportunities (incentive to stay employed, decreased burnout); and (3) learning needs (palliative, patient and family needs, older adults and dementia, acute nursing skills). Nurses who provide care to patients in their homes have very complex roles and responsibilities. In order to keep patients and nurses safe, standards of education for HCNs, beyond their basic education program, must be developed. These educational standards must be designed to address the complex medical needs of patients while making the educational opportunities accessible and value-added. Improving the CNE experience for HCNs has the potential to increase patient safety, improve care outcomes, increase nurse competence, improve retention, and decrease nurse burnout.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".