The Usefulness of Patient-Reported Outcomes and the Influence on Palliative Oncology Patients and Health Services
Bibliographic record
Abstract
BACKGROUND: Through the British Columbia, Prospective Outcomes and Support Initiative (POSI), registered nurses collect patient-reported outcome (PRO) data during telephone follow-up with palliative oncology patients. OBJECTIVE: The research objective was to describe the usefulness and influence of the nursing care provided through POSI follow-up on palliative patients and health services. METHODS: We used a qualitative interpretive description approach involving the collection and analysis of semistructured interview data with 20 palliative patients and 12 oncology nurses. All participant data were subjected to an inductively derived coding framework. Analytic categories were identified and iteratively revised through constant comparative techniques to develop representative themes. RESULTS: The accounts of patients and nurses suggest that telephone follow-up with PROs enabled the nurses to (1) focus on the priorities of patients experiencing complex health challenges, (2) manage complex symptoms, (3) ease the patient's transition home, and (4) improve access to and use of health services. Suggestions for improving POSI nurse follow-up centered on flexibility in the timing of the follow-up, creating dedicated POSI work assignments, and having additional time to personalize assessments and nursing care beyond the PRO questionnaires. CONCLUSIONS: Nursing care employing PROs via telephone follow-up can improve palliative cancer patients' quality of life and health service use. IMPLICATIONS FOR PRACTICE: Nurses are optimally positioned to use PROs following cancer treatment completion but require organizational resources and support to optimize patient and system outcomes.
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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 | 0.025 | 0.105 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".