Prevalence, Correlates, and Costs of Patients With Poor Adjustment to Mixed Cancers
Bibliographic record
Abstract
Approximately 2% to 3% of the Canadian society has experienced cancer. Literature indicates that there is poor adjustment to chronic illness. Individuals with poor adjustment to chronic illness have been found to disproportionately use more health services. The purpose of this study was to determine the prevalence, correlates, and costs associated with poor adjustment to mixed cancer. A consecutive sample (n = 171) of breast, lung, and prostate cancer patients at the Nova Scotia Regional Cancer Center were surveyed. Twenty-eight percent of the cancer group showed fair to poor adjustment to illness using the Psychological Adjustment to Illness Self-report Scale Psychological Adjustment to Illness Self-Report Scale raw score. Poor adjustment was moderately correlated with depression (r = 0.50, P < .0001) and evasive coping (r = 0.38, P < .0001) and unrelated to demographic variables. Depression explained 25% of the variance in poor adjustment to illness in regression analysis. Cancer patients with fair to poor adjustment to illness had statistically significantly higher annual healthcare expenditures (P < .002) than those with good adjustment to illness. Expenditure findings agree with previous literature on chronic illnesses. The prevalence of fair to poor adjustment in this cancer population using the Psychological Adjustment to Illness Self-Report Scale measure is similar to that reported for chronic illness to date, suggesting that only those with better adjustment consented to this study.
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".