Bibliographic record
Abstract
Abstract In the EAP field, utilization rates are an important concept routinely used as a descriptor of EAP success, yet there has been little formal research conducted in this area. In a study of 154 Canadian EAPs, 102 organizations reported their utilization rates along with how they defined both utilization and a case. Mean utilization rate was 9.2% with utilization being greater in organizations with a union where labor was involved in establishing the program, providing assistance in accessing the program and in managing the program through participation on a joint labor-management committee. Utilization rates were also found to be greater where there was an EAP policy in place and where ongoing program promotion occurred. However, what was also discovered was that most of these statistical conclusions were questionable as there was a lack of consistency in how utilization rates were calculated by various organizations, nor was there any agreement on what even constituted a case. This brings into question the utility of EAP utilization rates in any comparative program monitoring or evaluation. A comprehensive EAP Utilization Scorecard is offered as a response to this situation. The scorecard counts the actual number of employees, retirees and family members who use the EAP, either face-to-face, through telephone counselling or via e-counselling. Also presented is the idea of a new calculation, penetration rate. This value would include counselling offered by the EAP along with the other services, including group counselling, critical incident debriefings, consultations and mediations, workshops and seminars, peer referral contacts and telephone inquiries. This approach would provide a more comprehensive understanding of what EAPs do and would also allow for longitudinal program comparison as well as comparisons between programs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".