An important role for cancer nurses: Responding to psychosocial distress in cancer patients
Bibliographic record
Abstract
I diagnosed with cancer experience more than physical impacts. There are also emotional, psychosocial, spiritual, and practical consequences. Distress emerges as patients cope with the changes they face throughout their cancer journey. Although all patients experience distress, between 35-45% have clinically significant levels such as anxiety, depression, and adjustment difficulties. Early identification of distress and providing interventions to reduce this symptom is a standard of quality cancer care and a requirement of health services accreditation. Nurses have a critically important role to indentify distressed individuals, engage in relevant assessment, and provide interventions to manage distress. 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. Implementation of the programs has included relevant education of nurses, close attention to uptake and utilization of practice guidelines, a context of continuous quality improvement, and the use of rapid cycle evaluations. Cancer nurses are expected to respond to the standardized distress scores by opening conversations with items that are of concern to patients. Evaluation 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 beyond those related to tumor and side effects. Nurses are in an excellent position to respond to scores on a standardized distress screening tool as part of patient assessment. The assessments provide a foundation for individualized or tailored interventions. Margaret I. Fitch, J Nurs Care 2013, 2:3 http://dx.doi.org/10.4172/2167-1168.S1.002
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".