PCN229 - COMPARISON OF SICKNESS BENEFIT AND SICK LEAVE DURATION IN ONCOLOGY ACROSS COUNTRIES
Bibliographic record
Abstract
There is a growing acceptance of using the societal perspective in HTA and economic evaluations. Loss of work productivity and absenteeism are the key drivers of the costs within the societal perspective. Indirect costs are particularly relevant in oncology as most people diagnosed with cancer are working at the time of diagnosis and thus likely to take time out of the workforce for treatment, recuperation and rehabilitation. Indirect costs are also relevant for caregivers who need to take time away from work to care of their partner. Sick leave duration is influenced by countries’ sickness policy, so it is important to have recent information on these policies when estimating the impact of absenteeism. Sick leave management paths in the United States (US), Canada and Europe were reviewed through national websites and institutional reports. Information on short and long-term, disability and payment was extracted. Additionally, a targeted literature search of sick leave duration in oncology patients was conducted. There are large differences in sick leave policies across countries. European countries offer the longest and most generous compensation for sick leaves (up to 2 years or more). US and Canada offer no statutory right to receive contractual pay during sick leave, so employers design their policy. Duration of sick leave in oncology patients also varies significantly across countries. In Sweden, an average of 12.9 to 59.8 days in 5 common cancers was reported (depending on age and sex), while in the US, the estimated state-level median number of days of absenteeism per year among employed cancer patients was 6.1. Sickness policies vary across countries and need to be adjusted for when estimating the indirect cost of absenteeism. This is especially important for oncology, where indirect cost represents a sizeable portion of the total cost.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".