Bibliographic record
Abstract
Cancer is not only a debilitating disease; it can also have devastating effects on a patient’s mental health and quality of life. Although the literature shows that mortality rates may be improved with the provision of effective treatment, most psychotherapy provided to patients with cancer tends to be quite generic and not always effective. Very few research studies have investigated the provision of trauma-focused therapies to this underserved population. Eye movement desensitization and reprocessing (EMDR) therapy has well-established efficacy in the treatment of traumatic stress and preliminary evidence in the treatment of depression and anxiety. It is a very effective and accessible treatment for patients with cancer. Because it does not require homework, it is less demanding than many other forms of treatment. It can be provided on an intensive (twice) daily basis, making it available to patients traveling from out of town for their cancer treatment. For those patients unable to manage trauma-focused treatment during a difficult time, it can be used to enhance personal resources. EMDR therapy can be administered in individual, group, and couple formats and is suitable for children as well as adults. New research provides promising results for the application of EMDR for patients with cancer.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".