A Role for Cancer Nurses: Responding to Distress in Cancer Patients
Bibliographic record
Abstract
Background: Although all patients experience distress, between 35-45% experience clinically significant levels of distress such as anxiety, depression and adjustment difficulties. Early identification of distress and the provision of relevant interventions is a standard of quality cancer care. Nurses have a critically important role in identifying individuals with distress and providing interventions to manage distress.Objectives: Our objectives are to define the role of cancer nurses in responding to emotional distress experienced by cancer patients and to document the best practices for implementing a program of screening for distress in ambulatory settings.Methods: A programmatic approach to screening for distress (6th vital sign) has been implemented in several cancer facilities across Canada. The program includes protocols for screening, algorithms for assessment, and guidelines for evidenced based interventions. Introduction of the programs have included relevant education of nurses and close attention to uptake and utilization of practice guidelines. Implementation has been mounted within the context of continuous quality improvement and the use of rapid cycle evaluation.Results: Evaluation of successful program implementation has shown increased patient satisfaction with care. Patient concerns provide the focus for opening conversations with individuals and the basis for planning person-centered approaches to care. Patient concerns are identified through the screening maneuver beyond those related to tumor and side effect management. Nurses are in an excellent position to respond to scores on a standardized distress screening tool as part of their patient assessments. The assessments provide a foundation for individualized or tailored interventions.Conclusions: Using a concrete programmatic approach, including screening, focused evidence-based assessment and interventions, offers benefits in achieving person-centered care for cancer patients. In busy clinical settings, an intentional effort is needed to implement a programmatic approach to screening followed by appropriate assessment and intervention.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".