Delivery of cancer care via an outpatient telephone support line: A cross-sectional study of oncology nursing perspectives on quality and challenges.
Bibliographic record
Abstract
427 Background: Patient support lines (PSLs) help in triaging clinical problems, addressing patient queries and assist in navigating a complex multi-disciplinary oncology team. While providing support and training to nursing staff who operate these lines is key, there is limited data on their experience and feedback to guide this. Our objective was to identify areas of quality improvement for The Ottawa Hospital Cancer Centre (TOHCC) patient support lines. Methods: We conducted a cross-sectional study of oncology nurses’ (ONs’) perspectives on the provision of care via PSLs at a tertiary referral cancer center via an anonymous, descriptive survey. Measures collected included nursing/patient characteristics, nature of questions addressed by the PSL, patient/nursing satisfaction with the service, common challenges faced, and initiatives to improve the patient and nursing experience. Results: Seventy-one percent (30/42) of eligible nurses responded to the survey. The most common disease site, stage, issue, and symptom addressed were breast cancer, metastatic disease, treatment-related toxicity, and pain, respectively. Despite majority of nurses reporting personal and patient satisfaction with the care provided by the PSL, there was variance in the perceived appropriate use of PSL by physicians and patients. As such, fifty-nine percent (17/29) of nurses recommended redefining the responsibilities of the PSL to achieve its maximal potential, with 75% (6/8) ONs identifying high call volumes due to inappropriate questions as a barrier to care. Sixty percent (18/30) of nurses reported that having TOHCC-specific management plans for common issues would improve their experience, and the quality of care provided on PSL. Lastly, 80% (24/30) of nurses denied experiencing a reduction in PSL call volumes with increased patient access to their electronic medical record. Conclusions: Our study identified several important areas for improvement warranting further investigation, despite high reported rates of satisfaction with care provided on PSL. A need for TOHCC standardized management algorithms for common issues addressed on the PSL as well as increased physician and patient education to redefine goals of the PSL to address problems with high volume, and inappropriate calls was identified.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".