Delivering psychosocial support to family caregivers of cancer patients: Insights from Iranian psychosocial oncology professionals and family caregivers highlighting the need for change
Bibliographic record
Abstract
OBJECTIVES: Supporting a family member with cancer poses significant challenges for family caregivers, who have unmet supportive care needs. Psychosocial oncology professionals (PSOP) are often the primary source of support for cancer caregivers in Iran. Given the lack of supportive care resources, innovative strategies are needed to support caregivers. This study explores the views of PSOP and caregivers regarding the challenges, potential solutions, and the role of digital technologies in supporting caregivers. METHODS: Employing a qualitative descriptive design, we conducted individual interviews and focus groups with 30 participants (15 PSOPs and 15 caregivers), recruited from five settings in Tehran, Iran(2023-2024). All sessions were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. RESULTS: PSOP identified challenges in delivering psychosocial care to caregivers , including inconsistency, uncertainty, and fragmented use of technology. Their recommendations included flexible psychosocial care via blended multi-modal digital technologies, professional development opportunities, and formal recognition and integration within the oncology setting. Caregivers experiencing frustration with the healthcare system expressed a need for family-centered care, flexible psychosocial care, and organized peer support networks. SIGNIFICANCE OF RESULTS: Current psychosocial care in Iran is insufficient and misaligned with the preferences of PSOP and caregivers. PSOP and caregivers advocate for flexible psychosocial care through blended digital strategies. Public health strategists in Iran, as a low-resource setting with a family-centered context, should optimize resource utilization by prioritizing the training of PSOP, developing blended digital interventions, and leveraging trained peers to provide navigation and support to families, thereby easing the PSOP workload.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".