Model of care and pattern of nursing practice in ambulatory oncology
Bibliographic record
Abstract
BACKGROUND: The worldwide burden of cancer warrants more effective and efficient cancer care management strategies (World Health Organization, 2003). Model of ambulatory care, in particular, has tremendous implications on patient and administrative outcomes (Knight, 2007; Lynch, Marcone & Kagan, 2007; Moore, Johnson, Fortner & Houts, 2008). Nevertheless, little is known about the pattern of practices and the clinic operations in this setting. As part of the hospital's ambulatory redesign program, this survey aims to: a) review and describe existing models of ambulatory care employed by key national and international cancer centres; b) identify models or elements within the models that are appropriate to be adopted for best practice. METHODS: Semi-structured interviews with 10 senior management members at 10 centres (N = 10) were conducted. Participants were asked to describe: a) model of ambulatory care employed by their institution (e.g., scope and model of ambulatory nursing practice); and b) strategies used to manage common patient care related issues in their institutions. Interviews were recorded and analyzed according to the objectives of this survey. RESULTS: A majority of cancer centres employed models of care similar to the existing Hospital model of ambulatory care and are encountering similar challenges in care delivery. Therefore, it was not deemed appropriate to adopt any models from participating centres. CONCLUSION: It is crucial for administrators to report and share best practices to ensure high-quality care. The survey of current practice did not yield sufficient data to adequately support the implementation of any specific models at the hospital's ambulatory care settings. Nevertheless, findings from the present review support the principles proposed for the new hospital model of ambulatory care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".