Practice requirements for psychotherapeutic treatment of cancer patients in the outpatient setting—A survey among certified psychotherapists in Germany
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to delineate the challenges that psychotherapists encounter when they treat cancer patients and how they organise their practices to be able to treat them. METHODS: A random sample of certified psychotherapists, licensed by the health authorities, with training in psycho-oncology, was asked to complete a questionnaire covering the following issues: therapists' qualifications, organisation of the practice, dealing with appointment cancellations, financing, and networking. Practices with ≥50% cancer patients in their patient load were defined as "practices specialising in cancer" (PSC) and were compared to practices with a smaller proportion of cancer patients (non-PSC). RESULTS: Of 120 contacted therapists, 83 replied and 77 were eligible. The median waiting time for a first consultation was 10 days in PSC and 14 days in non-PSC (P = .05). Seventy-five of PSC and 56% of non-PSC can offer psychotherapy within 4 weeks. Time spent on dealing with the social problems of the patients was higher in PSC than in non-PSC (P = .04). They spent also more time communicating with other health care professionals such as private practice oncologists (P = .001). Cancer patients need to cancel appointments more frequently than noncancer patients (58% vs 48% cancel ≥1× per quarter). Sixty-six percent of the psychotherapists do not ask for financial reimbursement of these sessions. CONCLUSION: Psychotherapy for cancer patients in the outpatient setting requires different organisation of the practice. Sessions are cancelled more frequently, waiting time is considerably shorter, and psychotherapists communicate more often with other health care providers than in general psychotherapy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".