Disparities in psychosocial cancer care: a report from the International Federation of Psycho‐oncology Societies
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to understand the characteristics of the International Federation of Psycho-oncology Societies (FPOS) and possible disparities in providing psychosocial care in countries where psycho-oncology societies exist. METHOD: A survey was conducted among 29 leaders of 28 countries represented within the FPOS by using a questionnaire covering (i) characteristics of the society; (ii) characteristics of the national health care system; (iii) level of implementation of psycho-oncology; and (iv) main problems of psycho-oncology in the country. RESULTS: Twenty-six (90%) FPOS returned the questionnaires. One-third reported to have links with and support from their government, while almost all had links with other scientific societies. The FPOS varied in their composition of members' professions. Psychosocial care provision was covered by state-funded health services in a minority of countries. Disparities between countries arose from different causes and were problematic in some parts of the world (eg, Africa and SE Asia). Elsewhere (eg, Southern Europe and Eastern Europe), austerity policies were reportedly responsible for resource shortages with negative consequences on psychosocial cancer care. Half of FPOS rated themselves to be integrated into mainstream provision of care, although lack of funding was the most common complain. CONCLUSIONS: The development and implementation of psycho-oncology is fragmented and undeveloped, particularly in some parts of the world. More effort is needed at national level by strong coalitions with oncology societies, better national research initiatives, cancer plans, and patient advocacy, as well as by stronger partnership with international organizations (eg, World Health Organization and Union for International Cancer Control).
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".