Workplace Support Systems in Small- and Medium-Sized Companies for Employees Receiving Medical Treatment in Japan
Bibliographic record
Abstract
BACKGROUND: Maintaining one’s current job is important for patients. Few studies have investigated the presence of support systems in small- and medium-sized companies to help balance the therapeutic needs and occupational roles of workers in Japan. AIMS: To understand whether small- and medium-sized companies in Japan have established workplace policies to help employees with chronic disease balance medical treatment and professional life. METHODS: We surveyed a sample of small- and medium-sized companies in Japan identified from a large database of corporate credit and marketing research companies between February and March 2017. A questionnaire addressed workplace policies that supported employees’ medical treatments and professional lives, such as flexible work arrangements and the preparation of manuals and forms to facilitate communication with treating physicians. RESULTS: Of the 4158 companies initially contacted, 1140 companies (27%) responded to the survey. Of the valid respondents, 21% of the workplaces reported having established sufficient office rules to address employee’s necessary medical needs. Approximately half of the workplaces (53%) shared that they had a system in place to provide temporary medical leave for employees with chronic diseases. Few (12%) workplaces had established a process for having a trial return to work after a period of absence due to a medical condition. CONCLUSIONS: Currently, a minority of small- and medium-sized companies in Japan have established workplace policies to address the medical needs of employees with chronic diseases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".