Best Practices in Oncology Distress Management: Beyond the Screen
Bibliographic record
Abstract
The field of psychosocial oncology is a young discipline with a rapidly expanding evidence base. Over the past few decades, several lines of research have established that psychosocial problems, such as anxiety, depression, post-traumatic stress, fatigue, sexual dysfunction, and cognitive complaints, are common and consequential in patients with cancer. The word "distress" was chosen deliberately to capture a broad concept; consequently, distress screening is meant to function as an initial step in the more targeted evaluation of the source(s) of the patient's distress. In 2015, the American College of Surgeons' Commission on Cancer mandated psychosocial distress screening as part of their accreditation process. Similar screening requirements are in place internationally, including in Canada, where screening for distress is endorsed as the sixth vital sign and a standard of care that must be met by any Canadian health care organization providing cancer services that seeks to be accredited. Over the past few years, cancer centers around the world have been exploring optimum ways to implement and evaluate distress screening initiatives. This paper presents three approaches to distress screening implementation: (1) a model that incorporates the importance of shared values, perceived benefits, and relevant outcomes in the implementation of distress management protocols; (2) a Canadian knowledge translation application to distress screening, including triage considerations and interventions; and (3) a novel approach to distress management via the use of a mobile application to manage post-traumatic stress symptoms. In closing, future opportunities and challenges associated with the emergence of technology will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".