Refocusing cancer supportive care: a framework for integrated cancer care
Bibliographic record
Abstract
OBJECTIVE: Cancer supportive care comprises an integrative field of multidisciplinary services necessary for people affected by cancer to manage the impact of their disease and treatment and achieve optimal health outcomes. The concept of supportive care, largely driven by Margaret Fitch's seminal supportive care framework, was developed with the intent to provide health service planners with a conceptual platform to plan and deliver services. However, over time, this concept has been eroded, impacting implementation and practice of supportive care. This study therefore aimed to examine expert contemporary views of supportive care with the view to refocusing the definition and conceptual framework of cancer supportive care to enhance relevance to present-day cancer care. METHODS: A two-round online modified reactive Delphi survey was employed to achieve consensus regarding terminology to develop a contemporary conceptual framework. A listing of relevant cancer supportive care terms identified through a scoping review were presented for assessment by experts. Terms that achieved ≥ 75% expert agreement as 'necessary' were then assessed using Theory of Change (ToC) to develop consensus statements and a conceptual framework. RESULTS: A total of 55 experts in cancer control with experience in developing, advising on, delivering, or receiving supportive care in cancer took part in the Delphi surveys. Expert consensus assessed current terminology via Delphi round 1, with 124 terms deemed relevant and 'necessary' per pre-specified criteria. ToC was applied to consensus terms to develop three key statements of definition, and a comprehensive conceptual framework, which were presented for expert consensus review in Delphi round 2. CONCLUSION: Finalised definitions and conceptual framework are strongly aligned with relevant international policy and advocacy documents, and strengthen focus on early identification, timely intervention, multidisciplinary collaboration, and end-to-end, cross-sector, cancer supportive 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 | 0.042 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".