A Concept Analysis of Oral Anticancer Agent Self-management
Bibliographic record
Abstract
BACKGROUND: The rapid development and adoption of oral anticancer agents (OAAs) for cancer management have shifted patients' roles from recipient to owner of their care delivery, assuming their responsibilities for self-managing their OAA treatments at home, while the concept of oral anticancer agent self-management (OAA-SM) has not been well clarified and defined. OBJECTIVE: This study was to clarify the concept of OAA-SM and identify major components, influential factors, and consequences of OAA-SM, as well as propose a representative conceptual model of OAA-SM. METHODS: A literature review was conducted concerning the concept and application of OAA-SM. The Walker and Avant method for concept analysis was utilized to guide the examination of OAA-SM. RESULTS: OAA-SM is a multifaceted and dynamic process that requires continuous adaptation by patients as multiple self-management challenges can emerge throughout OAA treatments. The defining attributes of OAA-SM include OAA adherence, adverse-effect self-management, patient-provider communication, and OAA safe storage, handling, and administration practices. Oral anticancer agent-SM is potentially influenced by a variety of patient-related, OAA-related, and healthcare system factors. Effective OAA-SM is associated with better patient and healthcare outcomes. CONCLUSIONS: The clarification of the concept of OAA-SM and the identification of attributes of OAA-SM and their interrelationships contribute to the body of knowledge in OAA-SM. IMPLICATIONS FOR PRACTICE: This concept analysis provides the foundation to increase healthcare providers' understanding of patients' needs for OAA-SM support and guides the development of patient-centered interventions to empower and engage patients and their families in effective OAA-SM, and improve patients' quality of life and care.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".