How Do Nursing Students Perceive the Needs of Older Clients? Addressing a Knowledge Gap
Bibliographic record
Abstract
Background. Many nurse educators understand that students need to embrace the challenges and rewards of working with older clients. Yet, they might wonder how they can help students to develop and what is the specialized knowledge necessary to care for older clients. Question. How do students perceive the nursing needs of older adults? Method. A qualitative descriptive study was undertaken. Data collection occurred through semistructured interviews (9 students) and one focus group (8 students) using a photoelicitation technique. The researchers used a descriptive approach to analyze the data. Findings. Six themes emerged from the data: ask the older client!; physiology rules; personal, not professional; who can validate?; hierarchy of needs; and help us learn. Conclusion. Participants relied upon previous patterns of learning, primarily experiential, and on the views of health care colleagues in clinical practice to make decisions about the health needs of older clients. Participants clearly recognized the need to and significance of understanding the health care requirements of older clients. Findings have implications for how the care of older clients is introduced into nursing education programs.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".