A Cross-Sectional Population-Based Survey Looking At The Impact Of Cancer Survivorship Care Plans On Meeting The Needs Of Cancer Survivors
Bibliographic record
Abstract
Introduction: In 2005, the Institute of Medicine endorsed survivorship care plans (SCP) with insufficient evidence under the assumption that SCPs would improve care. To date, there have been numerous studies on their effectiveness with mostly inconclusive results. The purpose of this study was to determine the impact of receiving a survivorship care plan (SCP) on meeting cancer survivorsu2019 overall, informational, physical, emotional and practical needs. It was hypothesized that those who received a SCP would have greater needs met than those who did not receive a SCP. Methods: All Nova Scotia survivors who met specific inclusion criteria and unmet the exclusion criteria were identified from the Nova Scotia Cancer Registry and sent the 83-item survey to assess experiences and needs across five domains (overall, informational, physical, emotional and practical). Descriptive statistics (frequencies, percentages) and Chi-square analyses were used to examine and report survey findings.Results: The response rate was 44.6%, with 1514 respondents. SCPs were significantly associated (p<0.00001) with receiving timely help and support to meet survivorsu2019 overall, informational, physical, emotional and practical needs post-treatment. For the most part, survivorsu2019 clinical characteristics, such as cancer type, time since treatment, chronic comorbidities and metastases, did not result in differences among the five outcomes.Conclusions: Those who received a SCP reported higher agreement on all five outcomes in comparison to those who did not receive a SCP. Further work should evaluate the delivery of SCPs and the components of SCPs that are most likely to contribute to positive survivor outcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".