The Glass Is Filling: An Examination of Employee Assistance Program Evaluations in the First Decade of the New Millennium
Bibliographic record
Abstract
Five electronic databases were searched using the key words Employee Assistance, research, and evaluation for articles published from 2000 to 2009 along with a manual search of the two prominent journals in the Employee Assistance field. Forty-two evaluations were found that were categorized using Macdonald's structure into four groups: needs assessments (n = 2), program development (case study) (n = 21), outcome (n = 10), and process (n = 9). Although the majority of evaluations were conducted in the United States (n = 29), there was a distinct international component with studies from Australia (n = 1), Canada (n = 5), Israel (n = 1), Japan (n = 1), South Africa (n = 2), South Korea (n = 1), and the United Kingdom (n = 2) also being published during the first 10 years of the new millennium. Evaluations were conducted upon programs delivered across the entire helping continuum: by peers, professionals working for the organization, and external providers as well as joint internal-external service delivery models. A broad range of methodologies were employed that demonstrated in general that the Employee Assistance Programs (EAPs) that were reviewed produced positive outcomes including saving organizations money as well as in producing positive change in those who sought counseling through their auspices. However, as well as describing new initiatives, program evolution, and offering insights into how specific programs could be further enhanced, broader themes were also examined such as who is and is not availing themselves of EAP services and the stigma that some still feel in seeking help through EAPs.
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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 | 0.147 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.068 | 0.076 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".