Global psycho‐oncology in low middle‐income countries: Challenges and opportunities
Bibliographic record
Abstract
OBJECTIVES: This Special Issue of Psycho-Oncology is focused on challenges and opportunities in the provision of psychosocial care to patients in low and middle-income countries (LMICs). The aim is to highlight global disparities and inequity in the provision of evidence-based, culturally-sensitive and timely psychosocial care and to showcase the work of researchers and practitioners to address this gap. We hope that this Issue will help to advance the psychological and social dimensions of cancer care in all parts of the world. METHODS: The focus of the papers is on research and clinical innovations in LMICs that target the psychological, social and cultural dimensions of cancer and on interventions to improve or maintain the psychological well-being, social functioning and/or quality of life of those who are affected and their families. RESULTS: These papers draw attention to guidelines, resource needs, clinical service evaluation, emerging research and knowledge translation within LMICs that advance knowledge and implementation in the field of psycho-oncology. CONCLUSIONS: Innovations and advances in psycho-oncology are emerging from LMICs to enhance the care of patients with cancer and their families in these regions and in all parts of the world. A sustained global initiative is now needed to ensure that guidelines for such care are routinely included in global, national and local cancer control plans and that essential resources and attention are directed to implement them.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".