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Record W91194987 · doi:10.1177/216507990104900304

Roles and Value Added Contributions of the Occupational Health Nurse

2001· article· en· W91194987 on OpenAlexaff
Yvonne Nelson

Bibliographic record

VenueAAOHN Journal · 2001
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsCorporationNursingBusinessOccupational health nursingHealth careSample (material)Value (mathematics)Compensation (psychology)RehabilitationPublic relationsMarketingPsychologyMedicineHealth educationPolitical scienceEconomic growthPublic healthFinanceEconomics

Abstract

fetched live from OpenAlex

This study replicated a study by Martin (1993) to examine the corporate perceptions of the current and future roles and activities of the occupational health nurse and the nurse's value added contributions in the local operating units of a large Fortune 100 company. A descriptive design was used for this study. The sample consisted of 44 corporate officials. The study findings are consistent with Martin (1993) and suggested that management is aware of the traditional roles and activities of the nurse, including direct care, education and counseling, and case management. The desired future activities identified were trend analysis, conducting plant rounds, and developing health programs specific to the needs of the corporation. The activities identified as adding value to the corporation included: planning and developing educational programs specific to the needs of the corporation, supervising the provision of nursing care, assisting in rehabilitation of injured workers, and providing follow up on workers' compensation claims. Corporate officials are aware of the traditional roles of the nurse. However, they may not be aware of the newer nursing roles, such as trend analysis, research, and budget development.

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)0.006
metaresearch head score (Gemma)0.014
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.361
Teacher spread0.332 · 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

Citations16
Published2001
Admission routes1
Has abstractyes

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