Absenteeism Problems And Costs: Causes, Effects And Cures
Bibliographic record
Abstract
Employee absences are both costly and disruptive for business, and the trend has been increasing steadily over the years. Personal illness and family issues are cited as the primary reason for unplanned absences. Employers have been attempting to determine the validity of these illnesses and offer incentives and propose possible solutions to mitigate these absences, including those caused by family issues. Illness, family responsibilities, personal issues and stress all take a toll on the worker which in turn affects morale, absences and productivity in the workplace. Some sources including Statistics Canada cite that absenteeism approximates 15-20 percent of payroll (direct and indirect) costs. This is significant. Canada Newswire stated on May 23, 2008 that absenteeism translates into losses of over $16 billion in salary expenses. The purpose of this paper is to identify the leading factors of absenteeism, possible “cures” that exist for these factors, and present results of companies that have implemented programs to combat the problem of absenteeism. It is important that businesses determine if they in fact have an absenteeism problem and thus consider utilizing some of the proposed solutions offered in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".