Exploring the policy implementation of a holistic approach to cancer investigation in non-specific symptom pathways in England: An ethnographic study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore the policy implementation of non-specific symptom pathways within the English National Health Service. METHODS: = 54), patient shadowing, and document review. RESULTS: The study examined how the policy concept of 'holistic' care was understood and put into practice within four non-specific symptom pathways. Several challenges associated with providing holistic care were identified. One key challenge was the conflict between delivering holistic care and meeting timed targets, such as the Faster Diagnosis Standard, due to limited availability of imaging and diagnostic tools. The interpretation of a holistic approach varied among participants, with some acknowledging that the current model did not recognise holistic care beyond cancer exclusion. The findings also revealed a lack of clarity and differing opinions on the boundaries of holistic care, resulting in wide variation in NSS pathway implementation across health care providers. Additionally, holistic investigation of non-specific symptoms in younger patients were seen to pose difficulties due to younger patients' history of health anxiety or depression, as well as concerns over radiological risk exposure. CONCLUSIONS: The study highlights the complexity of implementing non-specific symptom pathways in light of standardised timed cancer targets and local cancer policies. There is a need for appropriately funded organisational models of care that prioritise holistic care in a timely manner over solely meeting cancer targets. Decision-makers should also consider the role of non-specific symptom pathways within the broader context of chronic disease management, with a particular emphasis on expanding diagnostic capacity.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".