Bibliographic record
Abstract
urrently, employees often experience difficulty balancing the demands of home and work-life.These difficulties often lead to increased stress or decreased employee productivity and well being.Society is increasingly recognizing that employers need to assist workers to manage the conflicting priorities of work and family responsibilities if they are to decrease costs related to the provision of employee benefits and services, and increase employee commitment to the workplace (AON Consulting, 1999; Canadian Fitness and Lifestyle Research Institute, 1998; MacBride-King, 1999c;McGovern, 1996).It is important for occupational health nurses to have a better understanding of work-life balance and the factors within the workplace that impact the achievement of balance.This knowledge will assist the occupational health nurse in the assessment of employee health status, the provision of appropriate nursing interventions, and the development and implementation of health promoting programs and policies within the workplace. PURPOSEThe purpose of this research study was to examine the role of the employer in supporting work-life balance within an industrial setting.The ultimate goal of this research was to collect data to be used in the development of strategies which will increase employee well being
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".