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Record W2045419041 · doi:10.1300/j022v18n03_03

A Review of Canadian EAP Policies

2003· review· en· W2045419041 on OpenAlexaffabout
Rick Csiernik

Bibliographic record

VenueEmployee Assistance Quarterly · 2003
Typereview
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsConfidentialityBusinessEmployee assistancePublic policyPublic administrationPublic relationsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract A review of 154 Canadian EAPs found that 130 organizations had developed formal policies governing the program while 24 had not. Organizations with a policy were larger in size and were more likely to be unionized. They were also more likely to have had their EAP initiated by a joint labour-management committee and to use peer supports and internal resources to deliver EAP services. Of those EAPs without a policy, a disproportionate number had been developed during the 1990s. EAPs that had not developed a policy were also more likely to have begun exclusively by management and were more likely to rely on a third-party provider for clinical and administrative services. Of the 130 programs with EAP policies, 80 provided copies to be analyzed. Policies ranged in size from one to 31 pages with varying levels of comprehensiveness. Using the EAP Policy Best Practices Guidelines, policies scored between 5% and 75% with a mean of 36.7%. The introductory statement of principles, including discussions of the range of problems to be covered, confidentiality and union/management endorsement, was typically the strongest area of the policies. Areas that typically required enhancement were program development and EAP program roles. The comprehensiveness of the EAP policy was correlated with the size of the organization but not with program utilization. Public sector policies tended to be more comprehensive though only 26 of the 80 policies received a score of 50% or greater.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.383
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2003
Admission routes2
Has abstractyes

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