Efficacy and medical cost offset of psychosocial interventions in cancer care: Making the case for economic analyses
Bibliographic record
Abstract
The burden of cancer in the worldwide context continues to grow, as incidence and mortality increase each year. Regardless of where they live, a significant proportion of cancer patients at all stages of the disease trajectory will suffer social, emotional and psychological morbidity as a result of their diagnosis and treatment. Psychosocial interventions have proven efficacious in helping patients and families overcome many of the challenges that arise consequent to a cancer diagnosis. Addressing psychosocial needs is an essential aspect of any model of adequate cancer care, however it may also prove to be a cornerstone in efforts to extend the reach of cost-effective cancer treatment to meet the growing global need. In order to set the stage for discussion of economic issues, this paper first briefly reviews the literature detailing the extent of distress and the efficacy of psychosocial treatments for cancer patients. This is followed by a summary of terminology and costing concepts in the economic evaluation of psychosocial treatments, and a review of the literature on medical cost offset in mental health, other medical populations, and in cancer patients. The literature clearly supports the notion that psychosocial interventions are not only effective, but also economical. Conclusions support adding costing data into evaluations of the efficacy of psychosocial treatments in order to detail the often present but usually overlooked long-term cost savings that may be accrued to overburdened health-care systems.
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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 | 0.027 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".