Expanding Psycho‐Oncology Services in India: Perspectives From Physicians in Cancer Care Centres
Bibliographic record
Abstract
BACKGROUND: Given the rising importance of mental health in current times, it also calls for attention to how cancer care services can be improved upon by integrating the emotional needs of patients and caregivers in India. The country's unique diversity and socio-cultural fabric presents unseen challenges; therefore, this study explores the enablers and barriers of integrating psycho-oncology services in India through the lens of physicians working in cancer care settings. METHODS: Semi-structured interviews were conducted with 20 physicians (medical oncologists, surgical oncologists, radiation oncologists, and palliative physicians) who had worked in cancer care along with a psycho-oncologist, over the past 2 years. The data were analysed using thematic analysis. RESULTS: Four major themes and 15 sub-themes emerged during the analysis. All physicians unanimously reported that psycho-oncology services have been an enabler in the cancer care continuum, as well as a key catalyst in improving their productivity, impacting the overall treatment outcome. However, the multifaceted problem space requires national-level interventions to streamline an equitable delivery of psycho-oncology services across India. Major and sub-themes were identified and documented along with the text excerpts in the table. CONCLUSION: This study highlighted the much-needed appreciation of the intangible value added by early interventions from psycho-oncology professionals in India, as expressed by the beneficiaries of this service. The study also revealed the potential challenges hindering its growth, and perspectives from professionals across the country reinforced the need for novel solutions that address unique challenges while embracing its diversity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".