Implementing a nurse‐led screening clinic for symptom distress with community‐based referral for cancer survivors: A feasibility study
Bibliographic record
Abstract
INTRODUCTION: This prospective, single-arm, pragmatic implementation study evaluated the feasibility of a nurse-led symptom-screening program embedded in routine oncology post-treatment outpatient clinics by assessing (1) the acceptance rate for symptom distress screening (SDS), (2) the prevalence of SDS cases, (3) the acceptance rate for community-based psychosocial support services, and (4) the effect of referred psychosocial support services on reducing symptom distress. METHODS: Using the modified Edmonton Symptom Assessment System (ESAS-r), we screened patients who recently completed cancer treatment. Patients screening positive for moderate-to-severe symptom distress were referred to a nurse-led community-based symptom-management program involving stepped-care symptom/psychosocial management interventions using a pre-defined triage system. Reassessments were conducted at 3-months and 9-months thereafter. The primary outcomes included SDS acceptance rate, SDS case prevalence, intervention acceptance rate, and ESAS-r score change over time. RESULTS: Overall, 2988/3742(80%) eligible patients consented to SDS, with 970(32%) reporting ≥1 ESAS-r symptom as moderate-to-severe (caseness). All cases received psychoeducational material, 673/970(69%) accepted psychosocial support service referrals. Among 328 patients completing both reassessments, ESAS-r scores improved significantly over time (p < 0.0001); 101(30.8%) of patients remained ESAS cases throughout the study, 112(34.1%) recovered at 3-month post-baseline, an additional 72(22%) recovered at 9-month post-baseline, while 43(12.2%) had resumed ESAS caseness at 9-month post-baseline. CONCLUSION: Nurse-led SDS programs with well-structured referral pathways to community-based services and continued monitoring are feasible and acceptable in cancer patients and may help in reducing symptom distress. We intend next to develop optimal strategies for SDS implementation and referral within routine cancer care services.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".