What Matters in Cancer Survivorship Research? A Suite of Stakeholder-Relevant Outcomes
Bibliographic record
Abstract
The outcomes assessed in cancer survivorship research do not always match the outcomes that survivors and health system stakeholders identify as most important in the post-treatment follow-up period. This study sought to identify stakeholder-relevant outcomes pertinent to post-treatment follow-up care interventions. We conducted a descriptive qualitative study using semi-structured telephone interviews with stakeholders (survivors, family/friend caregivers, oncology providers, primary care providers, and cancer system decision-/policy-makers) across Canada. Data analysis involved coding, grouping, detailing, and comparing the data by using the techniques commonly employed in descriptive qualitative research. Forty-four participants took part in this study: 11 survivors, seven family/friend caregivers, 18 health care providers, and eight decision-makers. Thirteen stakeholder-relevant outcomes were identified across participants and categorized into five outcome domains: psychosocial, physical, economic, informational, and patterns and quality of care. In the psychosocial domain, one's reintegration after cancer treatment was described by all stakeholder groups as one of the most important challenges faced by survivors and identified as a priority outcome to address in future research. The outcomes identified in this study provide a succinct suite of stakeholder-relevant outcomes, common across cancer types and populations, that should be used in future research on cancer survivorship care.
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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 | 0.241 | 0.276 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".