Psychosocial status of Hungarian cancer patients. A descriptive study
Bibliographic record
Abstract
INTRODUCTION: Psychosocial status of cancer patients is still understudied in Hungary. AIM: The aim of the authors was to obtain current information on the mental and social status of this patient group. METHOD: Altogether, 1070 cancer patients with a wide range of cancer types were included in the study (30.0% male; age: 55.9 ± 11.0 years). RESULTS: A large part of the patients had serious financial difficulties and 41.3% of them were struggling with at least one more comorbid chronic disease. Further, 52.2% of the patients reported at least moderate anxiety or depression, while the occurrence of suicidal thoughts was almost three times higher among them than in the Hungarian normal population (13.0% vs. 4.6%). Level of perceived social support was also lower than the population standards and 61.6% of the patients reported willingness to benefit from professional psychological support. Quality of social life of the patients deteriorated with time after cancer diagnosis. A positive phenomenon, however, was that the primary coping style reported was active problem solving. CONCLUSIONS: The authors conclude that it is necessary to screen cancer patients for psychosocial difficulties and to establish conditions for their adequate mental and social care in Hungary.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".