Stakeholder-Identified Interventions to Address Cancer Survivors’ Psychosocial Needs after Completing Treatment
Bibliographic record
Abstract
The interventions used in cancer-survivorship care do not always address outcomes important to survivors. This study sought to understand stakeholders' views on the key concerns of cancer survivors after treatment and the interventions needed to meet survivors' and families' psychosocial needs after completing cancer treatment. We conducted a descriptive qualitative study using semi-structured interviews with stakeholders (survivors, family/friend caregivers, oncology providers, primary care providers, and cancer system decision-/policy-makers) from across Canada. For the data analysis, we used techniques commonly employed in descriptive qualitative research, such as coding, grouping, detailing, and comparing the data. There were 44 study participants: 11 survivors, seven family/friend caregivers, 18 health care providers, and eight decision-/policy-makers. Stakeholder-relevant interventions to address survivors' psychosocial needs were categorized into five groups, as follows: information provision, peer support, navigation, knowledge translation interventions, and caregiver-specific supports. These findings, particularly interventions that deliver timely and relevant information about the post-treatment period and knowledge translation interventions that strive to integrate effective tools and programs into survivorship care, have implications for future research and practice.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".