What are the supportive care service needs of recently diagnosed cancer patients? Results of a population based study
Bibliographic record
Abstract
8096 Background: Previous studies have suggested that a substantial proportion of recently diagnosed cancer patients may have unmet supportive care needs. As part of a cancer supportive care initiative in the province of Ontario Canada, we evaluated patients’ need, use and access to supportive care services. Methods: A prospective cross-sectional population based survey of patients with breast, colorectal and prostate cancers randomly selected from a provincial registry who were within 12 months of diagnosis was performed in 3 representative regions of the province of Ontario, Canada between June 2001 and April 2003. A validated, self- completed supportive care questionnaire that reviewed 46 unique core supportive care services was mailed to patients. Respondents indicated whether a particular service type was needed and used during the previous 12 months. Need and use scores were tabulated by adding the number of services chosen (score range 0–46). Access was measured by the gap between need and use of services. Demographic variables were collected to study predictive factors for need and use. Results: Responses were obtained from 2733 patients representing an adjusted response rate of 76%. Mean respondent age was 65.9 years with 51% of respondents being female. 83.5% attended a cancer centre in the previous 12 months. Mean need score from all respondents was 10.8 (SD 6.9). Need scores appeared highest for Information, Psychological and Physical Services. On multivariate regression, a higher need was predicted by: lower age, gender (female), non-attendance at a cancer centre, lower socioeconomic status, and disease type (breast) (all P<.001). Mean use score for all respondents was 8.7 (SD 5.1). In fewer than 10% of respondents a gap between need and use was recorded. Conclusions: Cancer patients reported need and use of few specific supportive care services in this population based survey. In contrast to previous studies, access did not appear to be a significant problem based on a small gap between need and use of services. Patient reported needs varied by a number of demographic factors. These findings are important for the planning of supportive care services in cancer centres and communities. No significant financial relationships to disclose.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".