Understanding how cancer survivors’ needs and experiences after treatment impact their health care utilization: a survey-administrative health data linkage study.
Bibliographic record
Abstract
ObjectivesTo link population-based survey data to routinely collected administrative health data to enable investigation of how cancer survivors' ongoing physical, emotional, and practical needs and experiences after completing cancer treatment impact their healthcare utilization, including discharge from oncology to primary care. ApproachThe "Cancer Transitions Survey" is a population-based survey examining survivors' experiences and needs after completing cancer treatment. The survey was administered by the Nova Scotia Cancer Registry (NSCR) as part of a national study, the largest of its kind in Canada. Respondents included Nova Scotian survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1-3 years after treatment. Survey responses were linked to cancer registry, physicians' claims, hospitalization, and ambulatory care data. The data linkage provided a full four years of healthcare utilization data for each cancer survivor, beginning one year after their cancer diagnosis. Results1557 survivors responded to the survey and subsequently had their data linked. Collectively, breast, colorectal, and prostate cancer survivors represented 78.5% of survey respondents. Most respondents (65.3%) were 65 years of age or older and 69.8% had an existing co-morbid condition. Regression analyses are now being conducted to investigate whether the type and magnitude of post-treatment care needs, and the interventions (services and supports) received, impact health care utilization in the survivorship period, including discharge to primary care. ConclusionThis study represents a unique opportunity to link data unavailable in administrative health data: namely, self-reported needs and use of non-physician services and supports (e.g., support groups, counselling). As such, this dataset permits investigation of healthcare utilization and patterns of care that cannot be accomplished using administrative health data alone.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".