Whole of patient cancer care: bridging the gap between policy and practice
Bibliographic record
Abstract
Calls to improve the quality of care to cancer patients seek a change in practice that moves beyond a focussed disease management model to one that integrates psychosocial care into routine clinical services. In The Institute of Medicine’s (IOM) call for “Whole of Patient” care in cancer, the following were identified priorities: improved assessment for better identification of psychosocial needs; support for self management of illness; clinical linkages aligning patients more effectively to services (eg through clinical pathways; case management); coordinated inter-disciplinary services and systematic follow-up and evaluation. In 2016 the World Health Assembly adopted a policy advocating patient centred and integrated care. Both policies call for a move from a (clinical) provider-centred approach with a selective focus on the physical aspect of care to one that has embedded within it the inclusion of patient psychosocial needs and wellbeing.Specialist practice is encapsulated within professional roles and health service systems and evidence to date identifies the gaps between evidence-based policy direction and clinical practice in this field. Furthermore innovative models of integrated care are necessary to ensure applicability to diverse settings of patient care to overcome the well-recognised disparities in access to the full range of specialist-based cancer.This paper will provide a brief overview of evidence regarding identification of psychosocial needs of cancer patients within cancer services; review barriers to integrating evidence-based psychosocial care into routine cancer services and discuss examples of research focussing on improving integration of such care within cancer services: focussing on patient, clinician and health system factors. These include multicomponent health service interventions addressing the core elements of IOM guidelines with a focus on improving clinical coordination and pathways among existing services, building skills in psychosocial care among “front-line” cancer clinicians, improving patient access to self management resources, and systematic evaluation incorporating patient outcomes, clinician perspectives and health economic impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.105 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 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".