E-health tools in oncology nursing: Perceptions of nurses and contributions to patient care and advanced practice
Bibliographic record
Abstract
As oncology nurses confront a rapidly evolving field with increased workplace pressure, the integration of evidence-based connected health platforms within practice presents promise. This study explores nurses’ perceptions regarding the utility of e-health tools, with a focus on the Oncology Interactive Navigator (OIN TM ), as a potential contributor to their practice and interactions with patients. Focus groups with oncology nurses were conducted at two time points: prior to exposure to the OIN TM (T1, n=8) and four weeks post unrestricted tool access (T2, n=7). Using qualitative constant comparison analysis, three themes emerged: (1) Key factors driving e-health use are multidimensional and evolving; (2) Dual role of e-health in meeting patient needs and supporting practice; (3) E-health as a catalyst for professional development and networking. E-health is appealing to oncology nurses, as it serves to advance practice and support patient care. Future research should explore best practices for optimal clinical implementation among all stakeholders involved.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".