Cancer patients consultation patterns in primary care and levels of psychological morbidity: Findings from the Health Survey for England
Bibliographic record
Abstract
AIM: To determine the consultations patterns in general practice, for people with cancer and other chronic illnesses, and to assess the levels of psychological morbidity. METHODS: The following questions from the 1999 Health Survey for England were analysed: presence of a self-reported long-standing illness and its nature, numbers of contacts with general practitioner (GP) in the previous 2 weeks, contact with a GP in the previous year for anxiety/depression or a mental, nervous or emotional problem, presence of a self-reported long-standing illness of mental illness, anxiety or depression, and GHQ12 scores. For comparison purposes, data from respondents reporting having asthma, arthritis, diabetes, other long-standing illness, and no long-standing illness are presented. RESULTS: A third of respondents with cancer had contact with a GP in the last 2 weeks, which was slightly higher than the other illness group, however the pattern of attendances for those respondents who did consult were similar between groups. A quarter of people with cancer had spoken to a GP in the last year about being anxious/depressed, or about a mental, nervous or emotional problem. A third of cancer respondents reported high GHQ12 scores, but self-reported long-standing illness of 'mental illness/anxiety/depression' was low (4%). CONCLUSION: The findings suggest that psychological morbidity may be unrecognised in some cancer patients. There is potential for these symptoms to be identified and treated in primary care, especially given the ongoing nature of the patient-doctor relationship and the easy access primary care affords.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".