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Record W1527953099 · doi:10.1300/j490v21n01_02

What We Are Doing in the Employee Assistance Program

2006· article· en· W1527953099 on OpenAlexaboutno aff
Rick Csiernik

Bibliographic record

VenueJournal of Workplace Behavioral Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee assistanceConceptualizationWorkforceBusinessQuarter (Canadian coin)Work (physics)Public relationsMutual aidPsychologyPolitical scienceComputer scienceEconomic growthEngineeringEconomics

Abstract

fetched live from OpenAlex

Using the Integrated Model of Occupational Assistanceas a framework, a review of 88 organizations from across Canada with active Employee Assistance Programs (EAPs) was conducted. It was found that the majority of programs provide services primarily to meet individual employee needs with fewer EAPs offering services targeted at enhancing the overall wellness of the workplace. Some form of mutual aid-self help programming was a component of nearly half of the EAPs though less than one quarter of programs took this form of assistance and applied it to the organizational environment. Neither workforce size, location, sector, having an existing EAP policy or committee nor who the program initiator was, were associated with the provision of enhanced mutual aid or organizational-focused services. The survey did discover, however, that the evolution of standard and traditional EAP practice into a more integrated wellness-focused approach is no longer merely a theoretical conceptualization but has begun to be put into place to a limited extent at several diverse Canadian work sites.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.223
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.011
Scholarly communication0.0150.009
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.051
GPT teacher head0.441
Teacher spread0.390 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations11
Published2006
Admission routes1
Has abstractyes

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