Association between unmet needs and utilization of emergency services among cancer survivors in Canada.
Bibliographic record
Abstract
12131 Background: In 2016, the Canadian Partnership Against Cancer distributed surveys to over 40,000 cancer survivors to understand their experiences transitioning from primary cancer treatment to follow-up cancer care. Previously reported results of this survey identified that cancer survivors had high rates of unmet physical, emotional and practical needs. This study describes the association between these unmet needs and emergency services (ES) utilization within the first three years after cancer treatment. Methods: 13,319 respondents returned the survey (response rate 33%). Respondents in this study have non-metastatic breast, hematologic, colorectal, melanoma or prostate cancer. The association between self-reported unmet needs and ES utilization was assessed using multivariable logistic regression. High ES utilization was defined as accessing ES more than three times per year during the first three years after active cancer treatment. Results: 8,911 participants are included in this analysis; 80.5% reported at least one unmet practical, physical, and emotional need (n=7169, Table). A total of 3.9% (n=344) reported high ES utilization. Unmet needs were a significant predictor of high ES utilization (OR 1.75 95% CI 1.14-2.68 p=0.01). Other significant predictors of high ES utilization on the multivariable analysis included: not being able to identify a healthcare provider in charge of follow-up cancer care (OR 2.73 95% CI 1.32-5.65 p=0.01), high oncologist utilization (OR 3.08 95% CI 2.28-4.15 p<0.01), high primary care provider utilization (OR 2.3 95% CI 1.76-3.0 p<0.01), having a chronic condition (OR 1.6 95% CI 1.19-2.07 p<0.01), having colorectal cancer (OR 2.12 95% CI 1.31-3.44 p<0.01), being enrolled in a clinical trial (OR 1.54 95% CI 1.11-2.16 p=0.01), and rating follow-up cancer care coordination as fair or poor (OR 1.39 95% CI 1.03-1.86 p=0.03). Conclusions: Unmet needs are associated with high ES utilization in the first three years after cancer treatment. A better understanding of the reasons for this association is required to develop approaches to reduce potentially preventable ES utilization and improve perceived care needs among cancer survivors. [Table: see text]
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".