From evidence to implementation: The global challenge for psychosocial oncology
Bibliographic record
Abstract
The human dimensions of medical care were highlighted by such pioneering figures as Cicely Saunders, Elizabeth Kubler-Ross, and Jimmie Holland and their tireless advocacy helped to build an evidence base for psychosocial and palliative interventions. In that spirit, we studied physical and psychological distress in advanced cancer and modeled pathways to distress in this population. We considered acute stress disorder as the prototype for psychological disturbances following the acute onset of life-threatening disorders, showing that it occurred in one-third of patients after the diagnosis of acute leukemia. To treat and prevent these symptoms, we developed Emotion and Symptom-focused Engagement (EASE), an integrated psychotherapeutic and early palliative intervention. We showed that EASE reduced both traumatic stress and physical suffering in these patients and a large multi-center trial is now underway. We also identified symptoms of depression and hopelessness n one quarter of patients with metastatic and advanced cancer, with worsening toward the end of life. To alleviate this distress, we developed a brief supportive-expressive therapy, referred to as Managing Cancer and Living Meaningfully (CALM). We showed in a large RCT that CALM improves depression, distress related to dying and death, and preparation for the end of life. We have now launched a global initiative involving 20 sites to date across North and South America, Europe, Australia, and Asia to have CALM implemented routinely in cancer care. Such initiatives are needed to move psychosocial care in cancer from evidence to implementation and to fulfill the dream of Jimmie Holland that cancer care be as humanistic as it is effective.
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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 | 0.472 | 0.605 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.040 | 0.067 |
| Open science | 0.015 | 0.045 |
| Research integrity | 0.045 | 0.089 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".