Screening for Psychosocial Distress: A Brief Review with Implications for Oncology Nursing
Bibliographic record
Abstract
PURPOSE: Psychosocial care is an integral component of caring for individuals living with cancer. The identification of psychosocial distress has been acknowledged as a hallmark of quality cancer care, and screening for distress standards has been established in several countries. The purpose of this brief review is to highlight recent developments in screening for distress in oncology populations; to provide insight into significant trends in research and implementation; and to explore implications for oncology nursing practice. METHODS: This paper reports a brief review of the literature from March 2021 to July 2024 on the topic of screening for distress in oncology. The literature was accessed through PubMed and reviewed by two authors. Trends in the topics presented were identified independently and then discussed to achieve consensus. RESULTS: The search within the designated period produced 47 publications by authors in North America, Australasia, and Europe. Topic trends included the design and adaptation of tools for special populations, the use of technology, descriptions of programs, identification of benefits, challenges, and overcoming barriers to screening for distress. CONCLUSIONS: Screening for distress is endorsed as part of the provision of quality oncology care. Nurses have an important role in screening individuals at risk for developing psychosocial problems and acting to reduce the associated morbidity. By continuing to be informed and educated about the emerging developments in screening for distress, nurses can understand and overcome barriers to implementation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".