EAP utilization patterns and employee absenteeism: Results of an empirical, 3-year longitudinal study in a national Canadian retail corporation.
Bibliographic record
Abstract
Despite the increasing need for employee assistance program (EAP) providers and human resources (HR) departments to demonstrate outcomes resulting from the availability and use of EAP services, few empirical studies have examined the relationship between EAP utilization and objective organizational outcome measures. This study made use of a unique longitudinal archival data set to examine EAP utilization, the problems for which help was sought, and the relationship of EAP utilization to absenteeism over 3 consecutive years among all EAP-eligible (N 3,448) employees in all locations of a large national Canadian retail store. Patterns of usage were examined by gender and age with a clearly defined EAP utilization statistic. Most frequently, the reasons for help seeking were personal issues, marital/family problems, and (a distant third) work-related issues. Longitudinal hierarchical linear modeling (HLM) was used to examine the differences in yearly absentee hours between EAP users versus non-EAP users. The results showed that EAP users generally had higher rates of absenteeism than nonusers during the year in which EAP was used but (with some exceptions) did not differ from the non-EAP user groups in the year(s) before and after treatment. Implications for consulting psychology are suggested.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".